JP5143937B1 - Operation management support system and program - Google Patents

Abstract

An object of the present invention is to make it easy to grasp the range affected by irregularities generated in transportation.
In an operation management support system 100, a database 101 includes a flight information 111 indicating operation contents for each flight, and a takeover source flight and a takeover destination flight for each takeover performed between flights for a takeover element. The connection information 112 indicating that is stored. The determination unit 104 refers to the flight information 111 and the connection information 112, and extracts a set of handovers in which the operation content of the takeover source flight and the operation content of the takeover destination flight do not satisfy a predetermined operation condition, Based on a predetermined determination criterion, grouping of a takeover source flight and a takeover destination flight included in the extracted set of takeovers is performed. The output unit 105 outputs group information 114 indicating flights that make up one arbitrary group among the flights that have been grouped.
[Selection] Figure 1

Description

  The present invention relates to an operation management support system and a program.

  There are systems that manage the assignment of aircraft spots (see, for example, Patent Documents 1 and 2). There is also a system for managing the operation of railways (see, for example, Patent Document 3).

JP 2002-74437 A JP 2001-307300 A JP 2000-1168 A

  In public transport, grasp how irregularities (events different from the plan) affect (propagate) depending on the schedule plan, crew work plan, passenger transit status, and load change status (existence status of connections). It is required to operate while. At present, such information cannot be managed in a unified manner, and skilled workers respond while supplementing it manually, including the determination of how far it will affect. For this reason, it depends on the skills of the individual, and if there is a lack of consideration, there are problems such as unexpected delay expansion and effects on passengers' connection. Even the conventional system described above has a similar problem because it cannot fully grasp the range affected by irregularities.

  An object of the present invention is, for example, to make it easier to grasp a range affected by irregularities generated in transportation.

The operation management support system according to one aspect of the present invention is
An operation management support system that supports operation management of a moving object in a transportation facility,
For each flight that is a unit of operation of the moving body, the flight information indicating the operation content is stored in the storage device, and at least one of the transfer elements carried by the moving body and the moving body is performed between flights. A database that stores, with a storage device, takeover information indicating a takeover source flight and a takeover destination flight for each takeover;
With reference to the flight information and takeover information stored in the database, a set of takeovers in which the operation details of the takeover source flight and the takeover destination flights do not satisfy predetermined operation conditions are extracted, A determination unit that performs grouping of a takeover source flight and a takeover destination flight included in the extracted set of takeovers by the processing device based on the determination criteria;
An output unit that outputs group information indicating flights constituting one arbitrary group out of flights grouped by the determination unit by an output device;

In one embodiment of the present invention,
The determination unit performs the grouping so that the flights to be taken over belong to the same group and each flight belongs to only one group as the predetermined determination criterion.

In one embodiment of the present invention,
The determination unit has, as the predetermined determination criterion, a group exists for each flight that is not a takeover destination flight in any of the takeovers included in the extracted takeover set, and the flights to be taken over are in the same group. The grouping is performed so as to belong.

In one embodiment of the present invention,
The database shows the operation time as the operation content for each flight, and further stores the flight information indicating the planned time which is the time when the operation is planned in advance.
The determination unit, when the operation time of the takeover source flight is delayed from the planned time of the takeover source flight, and when the operation time of the takeover destination flight is delayed from the planned time of the takeover destination flight, the predetermined unit It is determined that the operating conditions are not met.

In one embodiment of the present invention,
In the case where a plurality of takeovers including different takeover flights are included in the extracted set of takeovers, the determination unit determines that the operation time of the takeover source flights among the plurality of takeovers is the takeover destination flights. So that only the takeover flights that are delayed by more than a predetermined time from the limit time when the takeover source flights should be operated or only the latest takeover flights belong to the same group. The grouping is performed.

In one embodiment of the present invention,
The database stores flight information indicating the actual operation time as the operation content for the already operated flights,
The operation management support system further includes:
With reference to the flight information and takeover information stored in the database, the takeover in which the actually operated flight is the takeover source flight and the flight not yet operated is the takeover destination flight is extracted. The extracted operation time is the predicted operation time for the flight whose predicted operation time is predicted each time the processing time is predicted by the processing device from the actual time of the transfer source flight, and the operation time is predicted. The flight information indicating the predicted time as the operation content is stored in the database, and if there is a takeover with the flight whose operation time is predicted as the takeover flight, the takeover is extracted, and the extracted takeover is A prediction unit is provided for predicting the operation time of the takeover flight from the predicted time by the processing device.

In one embodiment of the present invention,
For each takeover, the database further stores takeover information indicating a standard time taken for takeover,
For the extracted takeover, if the actual time or predicted time of the takeover source flight is not delayed from the plan time of the takeover source flight, the plan time of the takeover destination flight becomes the operation time of the takeover destination flight. If the actual time or predicted time of the flight of the takeover source is delayed from the planned time of the flight of the takeover source, an arbitrary time equal to or greater than the standard time is added to the actual time or predicted time of the flight of the takeover source. It is predicted that the time after the planned time of the takeover flight will be the operation time of the takeover flight.

In one embodiment of the present invention,
For each takeover, the database further stores takeover information indicating the type of takeover element,
The prediction unit predicts the operation time of the takeover flight for each takeover when the takeover flight and the takeover flight are common, and a plurality of takeovers with different types of takeover elements are extracted. The latest time among the predicted operation times is set as the predicted time of the destination flight.

In one embodiment of the present invention,
The type of the takeover element includes at least one of a passenger, a passenger, and a cargo carried by the mobile body.

In one embodiment of the present invention,
The forecasting unit predicts the operation time of the destination flight for each extracted takeover when extracting a plurality of takeovers in which the destination flight is common and the takeover source flights are different, and among the predicted operation time The latest time is set as the predicted time of the destination flight.

In one embodiment of the present invention,
The operation management support system further includes:
An input unit that receives input of update information for updating flight information or transfer information stored in the database by an input device, and updates the flight information or transfer information stored in the database,
The determination unit refers to the flight information and takeover information stored in the database each time the flight information or takeover information stored in the database is updated by the input unit, and newly sets a takeover set. Extraction is performed, and the succession source flights and the destination destination flights included in the extracted set of takeovers are grouped.

In one embodiment of the present invention,
The output unit represents, as the group information, a stool constituting the arbitrary one group by a figure, and within the arbitrary one group, for each takeover included in the set of takeovers extracted by the determination unit In addition, a diagram representing the relationship between the takeover source flight and the takeover destination flight with lines between figures is output.

A program according to one aspect of the present invention is:
A program that supports the operation management of moving objects in transportation,
For each flight that is a unit of operation of the moving body, the flight information indicating the operation content is stored in the storage device, and at least one of the transfer elements carried by the moving body and the moving body is performed between flights. For each takeover, a computer having a database for storing takeover information indicating a takeover source flight and a takeover destination flight by a storage device,
With reference to the flight information and takeover information stored in the database, a set of takeovers in which the operation details of the takeover source flight and the takeover destination flights do not satisfy predetermined operation conditions are extracted, A determination unit that performs grouping of a takeover source flight and a takeover destination flight included in the extracted set of takeovers by the processing device based on the determination criteria;
Among the flights that have been grouped by the determination unit, group information indicating flights that constitute one arbitrary group is made to function as an output unit that outputs the information using an output device.

  In one aspect of the present invention, in the operation management support system, the determination unit extracts a set of takeovers in which the operation details of the takeover source flight and the takeover destination flights do not satisfy a predetermined operation condition, Based on a predetermined determination criterion, grouping of a takeover source flight and a takeover destination flight included in the extracted set of takeovers is performed. And an output part outputs the group information which shows the flights which comprise arbitrary one group among the flights by which the determination part performed grouping. For this reason, according to one aspect of the present invention, it becomes easy to grasp the range affected by irregularities (an event different from the plan) generated in the transportation facility.

1 is a block diagram showing a configuration of an operation management support system according to Embodiment 1. FIG. FIG. 3 is a diagram illustrating an example of a hardware configuration of an operation management support system according to the first embodiment. 5 is a flowchart illustrating an example of an operation of the operation management support system according to the first embodiment. The figure which shows an example of the data structure of the input data and database of the input part which concern on Embodiment 1. FIG. FIG. 3 shows an example of a connection diagram according to the first embodiment. FIG. 3 shows an example of a delay cluster according to the first embodiment. FIG. 4 shows an example of a list of delay clusters according to the first embodiment. FIG. 4 shows an example of a delay propagation diagram according to the first embodiment. FIG. 4 shows an example of a delay propagation diagram according to the first embodiment. FIG. 6 shows an example of a delay cluster according to the second embodiment. FIG. 10 shows an example of a list of delay clusters according to the second embodiment. FIG. 6 is a diagram showing an example of a delay propagation diagram according to the second embodiment. FIG. 10 shows an example of a delay cluster according to the third embodiment. FIG. 10 shows an example of a list of delay clusters according to the third embodiment. FIG. 9 is a block diagram showing a configuration of an operation management support system according to Embodiment 4.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

Embodiment 1 FIG.
FIG. 1 is a block diagram showing a configuration of an operation management support system 100 according to the present embodiment.

  In FIG. 1, an operation management support system 100 is a system that supports aircraft operation management (in this application, “operation” and “operation” are not distinguished), and includes a database 101, an input unit 102, a prediction unit 103, A determination unit 104 and an output unit 105 are provided. Note that an aircraft is an example of a transportation facility and a moving body used in the transportation facility, and this embodiment can be applied to other transportation facilities and other traveling bodies. Examples of other transportation means include railways, ships, automobiles and the like. Examples of other moving bodies include trains for railways, ships for ships, buses and taxis for automobiles, mail, trucks for delivery and transportation, and the like. In this way, the mobile body may not only carry a person (passenger, passenger) but also carry a thing (cargo, luggage) or both a person and a thing.

  The operation management support system 100 includes hardware such as a processing device 191, a storage device 192, an input device 193, and an output device 194. The hardware is used by each part of the operation management support system 100. For example, the processing device 191 is used to perform calculation, processing, reading, writing, and the like of data and information in each unit of the operation management support system 100. The storage device 192 is used to store the data and information. The input device 193 is used to input the data and information, and the output device 194 is used to output the data and information.

  The database 101 stores the flight information 111 indicating the operation contents for each flight that is an operation unit of the aircraft by the storage device 192. In the present embodiment, the database 101 stores the flight information 111 indicating the operation time as the operation content and further indicating the planned time for each flight. The operation time includes an actual operation time that is an actual operation time and a predicted time that is an operation time predicted by the prediction unit 103 as described later. The planned time is the time when the operation is planned in advance. Here, it is assumed that the database 101 stores at least flight information 111 indicating planned times for all flights. In addition, the database 101 stores flight information 111 indicating actual times for flights that have already been operated, and stores flight information 111 indicating predicted times for flights whose operation times are predicted by the prediction unit 103. It shall be.

  For example, when a certain flight departs, it can be considered that the flight has been operated. In this case, the database 101 indicates the planned departure time for all flights, indicates the actual departure time for flights that have already been departed, and indicates flight information that indicates the predicted departure time for flights whose departure time is predicted by the prediction unit 103. 111 is stored. Alternatively, for example, when a certain flight arrives, it can be considered that the flight has been operated. In this case, the database 101 indicates the planned arrival time for all flights, indicates the actual arrival time for the arrived flights, and indicates the predicted arrival time for the flights whose arrival time is predicted by the prediction unit 103. 111 is stored. In addition, each flight may be considered to have been operated at any time / state other than the departure and arrival of each flight.

  The database 101 further stores, in the storage device 192, connection information 112 indicating the previous flight, the subsequent flight, the standard time, and the type of the takeover element for each connection connecting the flights. The connection information 112 is an example of takeover information. A connection represents a takeover performed between flights for a takeover element, and exists for each takeover. The takeover element is at least one of an aircraft (ie, equipment) and an object carried by the aircraft (eg, passenger, crew, cargo). As the types of takeover elements, for example, equipment, passengers, crew (a cockpit crew, i.e., flight crew) (an example of an occupant), CA (cabin attendant, i.e. a cabin crew) (an example of an occupant), and freight are considered ( In this case, there are five types). The previous flight is a flight that is operated in advance among a set of flights to be taken over, that is, a takeover source flight. A subsequent flight is a flight that is operated later among a set of flights to be taken over, that is, a takeover destination flight. The standard time is the time taken for takeover in normal operation work, and is determined for each takeover by an arbitrary method. For example, with regard to takeover having a specific attribute such as the type of takeover element, it is possible to determine, as experience, the minimum time taken to take over as the standard time of the corresponding takeover. Or, for example, for a certain period of time, take the statistics of the time taken for takeover with a specific attribute in common, and determine the result (average value, intermediate value, minimum value, etc.) as the standard time of the takeover can do.

  As described above, for each takeover, the database 101 stores takeover information indicating the takeover source flight, the takeover destination flight, the standard time, and the type of the takeover element in the storage device 192. If only one type of takeover element is targeted, the type of takeover element may be omitted in the takeover information.

  The input unit 102 receives input of update information 113 for updating the flight information 111 or the connection information 112 stored in the database 101 by the input device 193. Then, the input unit 102 updates the flight information 111 or connection information 112 stored in the database 101 based on the input update information 113.

  The prediction unit 103 performs delayed propagation prediction. Hereinafter, the operation of the delay propagation prediction by the prediction unit 103 will be described.

The prediction unit 103 refers to the flight information 111 and the connection information 112 stored in the database 101, and a connection in which a flight actually operated is a previous flight and a flight not yet operated is a subsequent flight. Is extracted by the processing device 191. Then, the prediction unit 103 predicts the operation time of the subsequent flight from the previous flight performance time by the processing device 191 for the extracted connection. In the present embodiment, for the extracted connection, the prediction unit 103 predicts that the planned time of the subsequent flight becomes the operating time of the subsequent flight if the actual time of the previous flight is not delayed from the planned time of the previous flight. On the other hand, for the extracted connection, if the previous flight actual time is behind the previous flight planned time, the prediction unit 103 adds an arbitrary time equal to or greater than the standard time to the previous flight actual time. The time after the time is predicted to be the operation time of the later flight. That is, when the previous flight has a delay, the prediction unit 103 does not simply predict that the subsequent flight will be delayed by the same amount as the previous flight. It is predicted that there will be no delay in the flight, or a smaller delay in the later flight than in the previous flight. At this time, the length of the takeover time to be shortened is determined so as to satisfy the following conditions (a) and (b), and is preferably determined so as to satisfy the following condition (c).
(A) The shortened takeover time should not be shorter than the standard time.
(B) The time (that is, the predicted time) at which the subsequent flight can be operated by shortening the takeover time should not be earlier than the planned time of the subsequent flight.
(C) The predicted time should be as close as possible to the planned time for the later flight.

  Each time the prediction unit 103 predicts the operation time, the flight information 111 indicating the prediction time is stored in the database 101 for the flight whose operation time (that is, the prediction time) is predicted. Further, each time the operation time is predicted, the prediction unit 103 refers to the flight information 111 and the connection information 112 stored in the database 101, and determines the flight whose operation time (that is, the predicted time) is predicted as the previous flight. If there is a connection to be performed, the processing device 191 extracts the connection. Then, the prediction unit 103 uses the processing device 191 to predict the operation time of the subsequent flight from the predicted time of the previous flight for the extracted connection. In the present embodiment, for the extracted connection, the prediction unit 103 predicts that the planned time of the subsequent flight will be the operating time of the subsequent flight unless the predicted time of the previous flight is delayed from the planned time of the previous flight. On the other hand, if the predicted time of the previous flight is delayed from the planned time of the previous flight for the extracted connection, the predicting unit 103 adds an arbitrary time equal to or greater than the standard time to the predicted time of the previous flight. The time after the time is predicted to be the operation time of the later flight. In other words, the prediction unit 103 reduces the takeover time to a later flight if possible even when a predicted delay occurs in the previous flight, as in the case where an actual delay occurs in the previous flight. Predicts that there will be no delay or that the later flight will be less delayed than the previous flight.

  Here, when the prediction unit 103 extracts a connection, there may be a case where a plurality of connections in which the previous flight and the subsequent flight are common and the types of takeover elements are different are extracted. Also, there may be a case where a plurality of connections are extracted that share a later flight but have different previous flights. In such a case, the prediction unit 103 predicts the operation time of the subsequent flight for each extracted connection, and sets the latest time among the predicted operation times as the predicted time of the subsequent flight.

  The operation of the delay propagation prediction by the prediction unit 103 has been described above.

  The determination unit 104 detects a delay cluster. The delay cluster is a group of flights that should be considered as a group of measures for delays related to each other, and there may be a plurality of delay clusters. The delay cluster detection operation by the determination unit 104 will be described below.

  The determination unit 104 refers to the flight information 111 and the connection information 112 stored in the database 101, and processes a set of connections in which the operation content of the previous flight and the operation content of the subsequent flight do not satisfy a predetermined operation condition. Extracted by the device 191. Then, the determination unit 104 uses the processing device 191 to group the previous flight and the subsequent flight of the connections included in the extracted set of connections based on a predetermined determination criterion. In the present embodiment, the determination unit 104 determines a predetermined operation condition when the operation time of the previous flight is delayed from the planned time of the previous flight and the operation time of the subsequent flight is delayed from the planned time of the subsequent flight. Is not satisfied by the processing device 191. That is, the determination unit 104 refers to the flight information 111 and connection information 112 stored in the database 101, and the actual time or predicted time of the previous flight is behind the planned time of the previous flight, and A set of connections whose predicted time is later than the planned time for the subsequent flight is extracted. Then, the determination unit 104 performs grouping so that the flights to be taken over belong to the same delay cluster and each flight belongs to only one delay cluster as a predetermined determination criterion. Details of the grouping method will be described later using a specific example.

  The delay cluster detection operation by the determination unit 104 has been described above.

  The output unit 105 outputs a delay propagation diagram. The delay propagation diagram corresponds to one delay cluster, and the stool constituting the corresponding delay cluster is represented by a figure (for example, a rectangle), and the connection connecting the stools is represented by a line (for example, a straight line). . Hereinafter, the output operation of the delay propagation diagram by the output unit 105 will be described.

  The output unit 105 uses the output device 194 to output group information 114 indicating flights that make up one arbitrary delay cluster among the flights grouped by the determination unit 104. In the present embodiment, the output unit 105 outputs a delay propagation diagram corresponding to any one delay cluster as the group information 114. As described above, the delay propagation diagram graphically represents the flights making up the corresponding delay cluster, and for each connection included in the set of connections extracted by the determination unit 104 in the delay cluster, It is a figure showing the relationship with a later flight by the line between figures. The delay propagation diagram corresponding to which delay cluster is output by the output unit 105 may be determined, for example, by the input unit 102 receiving a delay cluster selection operation from the user, or by another method. . As another method, for example, the output unit 105 automatically generates a delay cluster having the largest number of flights, a delay cluster in which the largest delay occurs, a delay cluster including a preset highly important flight, and the like. It is possible to choose.

  The output operation of the delay propagation diagram by the output unit 105 has been described above.

  As described above, in the present embodiment, when a delay occurs in one or more flights, the determination unit 104 determines which flight has an effect on which other flights, and the influence is exerted. Each of the ranges is defined as a delay cluster. Then, the output unit 105 displays a diagram showing which flight delay affects which other flight in any one delay cluster as a delay propagation diagram corresponding to the delay cluster. For this reason, according to the present embodiment, a user (for example, a person in charge of operation management of an airline) can easily grasp the range to which the influence of delay occurring on one or more flights is affected. . Therefore, it becomes easy to take measures against delay.

  The prediction unit 103 newly performs delay propagation prediction every time the flight information 111 or the connection information 112 stored in the database 101 is updated by the input unit 102. That is, the prediction unit 103 refers to the flight information 111 and the connection information 112 stored in the database 101 (updated) and predicts the operation time of a flight that has not yet been operated.

  Whenever the flight information 111 or the connection information 112 stored in the database 101 is updated by the input unit 102, the determination unit 104 newly detects a delay cluster based on a new delay propagation prediction result by the prediction unit 103. . That is, the determination unit 104 refers to the (updated) flight information 111 and the connection information 112 stored in the database 101 to newly extract a connection set. Then, the determination unit 104 groups the previous flight and the subsequent flight of the connections included in the extracted connection set.

  The output unit 105 outputs a new delay propagation diagram based on the detection result of the new delay cluster by the determination unit 104 every time the flight information 111 or the connection information 112 stored in the database 101 is updated by the input unit 102. To do. When a delay cluster is newly detected by the determination unit 104 while the output unit 105 is outputting a delay propagation diagram, for example, the input unit 102 determines which delay cluster the delay propagation diagram corresponds to. May be determined by receiving a selection operation of a new delay cluster from the user, or may be determined by another method. As another method, for example, the output unit 105 automatically generates a new delay cluster having the same or similar structure (for example, the most common flights) to the delay cluster corresponding to the delay propagation diagram being output. It is possible to choose. Or, as described above, for example, a new delay cluster with the largest number of flights, a new delay cluster with the largest delay, a new delay cluster with previously set flights of high importance, etc. It is conceivable that the output unit 105 selects automatically.

  In the present embodiment, the input unit 102 may accept input of the temporary update information 113 by the input device 193. In this case, the input unit 102 performs a temporary update of the flight information 111 or the connection information 112 stored in the database 101 according to the input temporary update information 113. The prediction unit 103 refers to the flight information 111 and the connection information 112 (after provisional update) stored in the database 101 and newly performs delay propagation prediction. Similarly, the determination unit 104 refers to the flight information 111 and the connection information 112 stored in the database 101 (after provisional update), and newly determines a new delay propagation prediction result by the prediction unit 103. Detect delay clusters. The output unit 105 newly outputs a delay propagation diagram based on the detection result of the new delay cluster by the determination unit 104. Thereafter, the input unit 102 causes the input device 193 to select whether or not to actually update (commit) the flight information 111 or the connection information 112 stored in the database 101 according to the temporary update information 113. And the input part 102 performs the actual update of the flight information 111 or the connection information 112 memorize | stored in the database 101 according to the temporary update information 113, only when it is selected to perform the actual update. As a result, the user confirms by simulation how the configuration of the delay cluster and the delay propagation diagram change when the flight information 111 (particularly the planned time) and the connection information 112 (particularly the standard time) are changed. It is possible to review the operation plan easily. In addition, since the simulation result can be immediately reflected in the actual data, the operation plan can be improved quickly and efficiently.

  In the present embodiment, every time a delay cluster is detected, the determination unit 104 may store information indicating the configuration of each delay cluster in the database 101 as history information of the delay cluster. In this case, the output unit 105 outputs the delay cluster history stored in the database 101 by the output device 194. Thus, the user can easily grasp how the range affected by the delay generated in one or more flights changes according to the change in the delay occurrence status. Therefore, it becomes easy to take measures against delay.

  FIG. 2 is a diagram illustrating an example of a hardware configuration of the operation management support system 100.

  In FIG. 2, the operation management support system 100 is a computer, which includes an LCD 901 (Liquid / Crystal / Display), a keyboard 902 (K / B), a mouse 903, an FDD 904 (Flexible Disk / Drive), and a CDD 905 (Compact / Disc / Drive). Drive) and a hardware device such as a printer 906 are provided. These hardware devices are connected by cables and signal lines. Instead of the LCD 901, a CRT (Cathode / Ray / Tube) or other display device may be used. Instead of the mouse 903, a touch panel, a touch pad, a trackball, a pen tablet, or other pointing devices may be used.

  The operation management support system 100 includes a CPU 911 (Central Processing Unit) that executes a program. The CPU 911 is an example of a processing device 191. The CPU 911 includes a ROM 913 (Read / Only / Memory), a RAM 914 (Random / Access / Memory), a communication board 915, an LCD 901, a keyboard 902, a mouse 903, an FDD 904, a CDD 905, a printer 906, and an HDD 920 (Hard / Disk) via a bus 912. Connected with Drive) to control these hardware devices. Instead of the HDD 920, a flash memory, an optical disk device, a memory card reader / writer, or other recording medium may be used.

  The RAM 914 is an example of a volatile memory. The ROM 913, the FDD 904, the CDD 905, and the HDD 920 are examples of nonvolatile memories. These are examples of the storage device 192. The communication board 915, the keyboard 902, the mouse 903, the FDD 904, and the CDD 905 are examples of the input device 193. The communication board 915, the LCD 901, and the printer 906 are examples of the output device 194.

  The communication board 915 is connected to a LAN (Local / Area / Network) or the like. The communication board 915 is not limited to a LAN, but is an IP-VPN (Internet, Protocol, Private, Network), a wide area LAN, an ATM (Asynchronous / Transfer / Mode) network, a WAN (Wide / Area / Network), or the Internet. It does not matter if it is connected to. LAN, WAN, and the Internet are examples of networks.

  The HDD 920 stores an operating system 921 (OS), a window system 922, a program group 923, and a file group 924. The programs in the program group 923 are executed by the CPU 911, the operating system 921, and the window system 922. The program group 923 includes programs that execute the functions described as “˜units” in the description of the present embodiment. The program is read and executed by the CPU 911. The file group 924 includes data, information, and signal values described as “˜data”, “˜information”, “˜ID (identifier)”, “˜flag”, and “˜result” in the description of this embodiment. And variable values and parameters are included as items of "~ file" and "~ table". “˜File” and “˜Table” are components of the database 101 and are stored in a recording medium such as the RAM 914 or the HDD 920. Data, information, signal values, variable values, and parameters stored in a recording medium such as the RAM 914 and the HDD 920 are read out to the main memory and the cache memory by the CPU 911 via a read / write circuit, and extracted, searched, referenced, compared, and calculated. It is used for processing (operation) of the CPU 911 such as calculation, control, output, printing and display. During the processing of the CPU 911 such as extraction, search, reference, comparison, calculation, calculation, control, output, printing, and display, data, information, signal values, variable values, and parameters are temporarily stored in the main memory, cache memory, and buffer memory. Remembered.

  The arrows in the block diagrams and flowcharts used in the description of this embodiment mainly indicate input / output of data and signals. Data and signals are recorded in memory such as RAM 914, FDD904 flexible disk (FD), CDD905 compact disk (CD), HDD920 magnetic disk, optical disk, DVD (Digital Versatile Disc), or other recording media Is done. Data and signals are transmitted by a bus 912, a signal line, a cable, or other transmission media.

  In the description of the present embodiment, what is described as “to part” may be “to circuit”, “to device”, “to device”, and “to step”, “to process”, “to”. ~ Procedure "," ~ process ". That is, what is described as “˜unit” may be realized by firmware stored in the ROM 913. Alternatively, what is described as “˜unit” may be realized only by software, or only by hardware such as an element, a device, a board, and wiring. Alternatively, what is described as “to part” may be realized by a combination of software and hardware, or a combination of software, hardware and firmware. Firmware and software are stored as programs in a recording medium such as a flexible disk, a compact disk, a magnetic disk, an optical disk, and a DVD. The program is read by the CPU 911 and executed by the CPU 911. That is, the program causes the computer to function as “to part” described in the description of the present embodiment. Or a program makes a computer perform the procedure and method of "-part" described by description of this Embodiment.

  FIG. 3 is a flowchart illustrating an example of the operation of the operation management support system 100 (the operation management support method according to the present embodiment, the processing procedure of the program according to the present embodiment).

  In step S101 in FIG. 3, the operation management support system 100 acquires the latest information from a plurality of external business systems. Details of step S101 will be described below.

  FIG. 4 is a diagram illustrating an example of data input to the input unit 102 and a data structure of the database 101.

  In FIG. 4, flight information 201, flight plan information 202, GH information 203, crew work information 204, CA work information 205, passenger information 206, and cargo information 207 are stored in a plurality of business systems connected to the operation management support system 100. The information is managed individually. The flight information 201 is information indicating an operation reference date, a flight number, a departure / arrival airport, an aircraft, an actual time, an actual time, and the like, which will be described later. The flight plan information 202 is information indicating an operation reference date, a flight number, a departure and arrival airport, an aircraft, a planned time, a standard time, a planned time, a countermeasure time, and the like, which will be described later. The GH information 203 is information indicating the progress of GH (Grand Handling), which is work (main body maintenance, etc.) performed on the ground for each flight. The crew work information 204 is information indicating crews on the flights. The CA work information 205 is information indicating the CA (or group of CAs) that is on board each flight. Passenger information 206 is information indicating passengers (passengers) boarding each flight. The cargo information 207 is information indicating the cargo (package) transported by each flight.

  In step S101, the input unit 102 first receives the latest information on flight information 201, flight plan information 202, GH information 203, crew work information 204, CA work information 205, passenger information 206, and cargo information 207 from each business system. Obtained by the input device 193 (accepts input of latest information). The latest information of the flight information 201, flight plan information 202, GH information 203, crew work information 204, CA work information 205, passenger information 206, and cargo information 207 is an example of the update information 113.

  Next, in step S101, the input unit 102 creates the flight information 111 and the connection information 112 by the processing device 191 based on the latest information acquired from each business system.

  Specifically, the input unit 102 generates flight information 111 corresponding to each flight from the latest information of the flight information 201, the flight plan information 202, and the GH information 203. As shown in FIG. 4, the flight information 111 includes, for example, an operation reference date, a flight number, a departure and arrival airport, an aircraft, a planned time, an actual time, an estimated time, an estimated time, passenger information, passenger information, cargo information, and operational restrictions. It is information which shows. The operation reference date is the date on which the corresponding flight is operated. The flight number is a number for uniquely identifying the corresponding flight. The departure and arrival airports are the departure airport and arrival airport of the corresponding flight. The aircraft is the equipment used for the corresponding flight. The planned time is the planned time (scheduled time) of departure and arrival of the corresponding flight. The actual time is the actual time of departure and arrival of the corresponding flight (actual time). The estimated time is the estimated arrival time of the corresponding flight. For example, if the corresponding flight is before landing, the estimated flight time, the latest flight plan (weather and airport) This is calculated by adding the estimated flight time (operation time) according to the plan taking into account the congestion situation of the city. In addition, for example, if the corresponding flight has already landed but is not in a spot (airport parking area), the expected time is determined based on the latest spot congestion, etc. It is obtained by adding the time of guidance to the expected spot. The predicted time is the predicted time of departure and arrival of the corresponding flight, and is calculated by delayed propagation prediction in step S103 described later. The occupant information is information regarding crew and CAs who are on the corresponding flight. Passenger information is information regarding passengers who board the corresponding flight. The cargo information is information regarding the cargo loaded on the corresponding flight. The operational restrictions are restrictions when using the airport. The predicted time is input in step S103, which will be described later, and the actual time is input when the corresponding flight arrives and departs. In addition, the departure airport and aircraft are in the planning stage before the departure of the corresponding flight, and are updated if there is a change at the departure. Similarly, the arrival airport is in the planning stage before the arrival of the corresponding flight and is updated if there is a change upon arrival.

  The input unit 102 uses the latest information on flight information 201, flight plan information 202, GH information 203, crew work information 204, CA work information 205, passenger information 206, cargo information 207, and generated flight information 111 to connect each connection. The connection information 112 corresponding to is generated. As shown in FIG. 4, the connection information 112 includes, for example, flight information 111 (actually information for identifying the flight information 111), previous flight, connection type, standard time, planned time, actual time, This is information indicating the expected time, predicted time, and countermeasure time. The flight information 111 is the flight information 111 of the subsequent flight of the corresponding connection. The previous flight is the flight number of the previous flight of the corresponding connection. The connection type is a type of the corresponding connection takeover element, for example, equipment, crew, CA, passenger, or cargo. The standard time is a standard time for taking over the corresponding connection. The planned time is a planned time (scheduled time) for taking over the corresponding connection. The actual time is the actual time (actual time) of the corresponding connection, and is input when the subsequent flight departs. The expected time is an estimated time for taking over the corresponding connection, and is calculated by a predetermined method from the progress of the GH. The predicted time is a predicted time for taking over the corresponding connection, and is automatically input by delayed propagation prediction in step S103 described later. The countermeasure time is a shortened time for taking over the corresponding connection, and is manually input.

  In step S101, next, the database 101 stores the flight information 111 and the connection information 112 created by the input unit 102 in the storage device 192. In the present embodiment, the connection information 112 includes information on the subsequent flight and includes the flight number of the previous flight. In addition to the flight information 111 of the flight (in fact, information for identifying the flight information 111), information indicating the flight number of the subsequent flight may be used instead of the flight number of the previous flight.

  In step S101, the input unit 102 may not only create new flight information 111 and connection information 112, but the input unit 102 may update the flight information 111 and connection information 112 already stored in the database 101. For example, it is conceivable that after a certain flight arrives, the input unit 102 acquires the latest flight information 201 indicating the actual time, the actual time, and the like. In addition, for example, it is conceivable that there is a change in the equipment of a certain flight and the scheduled departure and arrival times, and the input unit 102 acquires the changed flight plan information 202. In such a case, the input unit 102 appropriately updates the flight information 111 and the connection information 112 already stored in the database 101 based on the latest information acquired from each business system.

  In this example, connection information 112 generated for each connection is managed in the database 101. That is, the database 101 functions as a connection-oriented database.

  In step S102 of FIG. 3, the operation management support system 100 generates a connection diagram. The connection diagram is a diagram in which a flight operated in a predetermined period is represented by a graphic (for example, a rectangle), and a connection that connects the flights is represented by a line (for example, a straight line). Here, the connection diagram represents all flights operated on a certain day as a predetermined period, but the connection diagram indicates flights operated on multiple hours, multiple days, or other periods. It may represent. Details of step S102 will be described below.

  In step S <b> 102, the output unit 105 generates a connection diagram by the processing device 191 based on the flight information 111 and the connection information 112 stored in the database 101, and displays the screen on the output device 194.

  FIG. 5 is a diagram illustrating an example of a connection diagram.

In FIG. 5, a rectangular block represents a flight, and “◯” of “No. ○ flight” in the block represents a flight number. The arrow line represents a connection, and “Ship” (equipment), “Crew” (crew), “CA”, “PAX” (passenger), and “Cargo” (cargo) added to the line are connections. Represents the type. Each type of connection requires the following work (work) from the arrival of the previous flight to the departure of the subsequent flight.
(1) “Ship”: GH of the same equipment from the arrival of the previous flight to the departure of the subsequent flight. In the case of the first flight (the flight where the equipment is used first in a day), the GH from the arrival of the last flight of the previous day (the flight where the same equipment is used last in the day) to the departure of the first flight is treated as a connection. May be.
(2) “Crew”: Crew connection from the previous flight to the subsequent flight. Operations after the arrival of the previous flight, movement between spots (if the equipment is different), operations before departure of the subsequent flight, etc. are included.
(3) “CA”: Connection of CA from previous flight to subsequent flight. Operations after the arrival of the previous flight, movement between spots (if the equipment is different), operations before departure of the subsequent flight, etc. are included.
(4) “PAX”: Passenger connection from previous flight to subsequent flight. This includes getting off from the previous flight, moving between spots (if the equipment is different), boarding a subsequent flight, etc.
(5) “Cargo”: Reloading cargo from the previous flight to the subsequent flight. Includes removal from the previous flight, transfer between spots (if the equipment is different), loading on the subsequent flight, etc.

  In this case, block out (the aircraft leaves the spot) is treated as departure of the flight, and block in (aircraft enters the spot) is treated as arrival of the flight. For example, takeoff (the aircraft takes off) May be treated as the departure of a flight, or landing (the aircraft will land) may be treated as the arrival of a flight.

  In the example of FIG. 5, the database 101 has flight numbers “11”, “12”, “13”, “21”, “22”, “23”, “31”, “32”, “33”, “ It is assumed that flight information 111 indicating “41” and “42” and a common operation reference date “August 1, 2011” is stored.

  Therefore, in the connection diagram of FIG. 5, on August 1, 2011, the 11th flight, the 12th flight, the 13th flight, the 21st flight, the 22nd flight, the 23rd flight, the 31st flight, the 32nd flight, It is shown that flights 33, 41 and 42 are operated. For example, when the output unit 105 generates the connection diagram for which day is targeted, the input unit 102 may determine that the date input operation is received from the user, or the output unit 105 automatically selects the day. You may decide by doing.

  In the example of FIG. 5, the database 101 includes the flight reference date “August 1, 2011”, the flight information 111 indicating the flight number “12”, the previous flight “11”, and the connection type “Ship”. It is assumed that connection information 112 is stored. Further, it is assumed that the database 101 stores the connection information 112, the flight information 111, and the previous flight with the same flight information 112 indicating the connection type “Crew”. Further, the database 101 stores flight information 111 indicating a flight reference date “August 1, 2011” and flight number “22”, and stores connection information 112 indicating a previous flight “11” and a connection type “CA”. It shall be.

  Therefore, in the connection diagram of FIG. 5, there are two connections, “Ship” and “Crew”, in which the 11th flight is the previous flight and the 12th flight is the subsequent flight, the 11th flight is the previous flight, It is shown that there is a “CA” connection with 22 flights as a later flight. The same applies to other connections.

  In step S103 of FIG. 3, the operation management support system 100 performs delay propagation prediction (PERT calculation). Details of step S103 will be described below.

  In step S <b> 103, first, the prediction unit 103 identifies a portion (feces) that does not require prediction calculation by the processing device 191. With regard to block-in, flights with actual values (actual arrival times) or flights with expected values (expected arrival times) are used as they are, and are excluded from prediction calculation targets. In addition, for a flight that has no previous flight (the first flight) and has no actual value for blockout, the planned value (planned time of arrival) is used as it is, and is excluded from the prediction calculation target.

  In step S103, next, the prediction unit 103 determines the block-in time of the previous flight (the estimated time when there is no actual arrival time, the estimated time when there is no actual time, and there is no estimated time) for the specified portion (flight) requiring the prediction calculation. The block time (departure time) of the subsequent flight from the predicted time, the planned time for the first flight) and the time required for the connection (planned time, or if the planned time is delayed, the time shortened with the standard time as the lower limit) (Predicted time) is calculated by the processing device 191. Then, the predicting unit 103 writes the calculated post-out block-out time in the flight information 111 of the database 101.

  The standard time for work is defined in the connection, and if the required time as planned (planned time) is used and the departure of the later flight is delayed, the prediction unit 103 automatically sets the required time to the standard time. Shorten with and use. Although it may be always reduced to the standard time, as described above, it is preferable that the predicted block-out time is not advanced ahead of the planned time. That is, it is desirable that the time (estimated time) set for the connection is the longest time within the range of the standard time or more and the planned time or less, and no delay occurs in the subsequent flight.

  In the example of FIG. 5, the database 101 shows the flight reference date “August 1, 2011”, flight number “41”, scheduled arrival time “9:00”, and actual arrival time “9:15”. It is assumed that information 111 is stored. In addition, the database 101 stores the operation reference date “August 1, 2011”, the flight number “42”, the planned departure time “10:00”, the planned arrival time “11:00”, and the actual departure time “none”. And the connection information 112 indicating the previous flight “41”, the connection type “Cargo”, the standard time “40 minutes”, and the planned time “60 minutes”.

  For this reason, the prediction unit 103 extracts a connection of “Cargo” in which the 41st flight actually operated is the previous flight and the 42nd flight not yet operated is the subsequent flight. With this connection, the arrival of the previous flight is delayed by 15 minutes, so if the time taken to take over the cargo remains at the planned time, the departure of the subsequent flight will also be delayed by 15 minutes. Therefore, the prediction unit 103 adds “10:00” obtained by adding the time “45 minutes” satisfying the above conditions (a) to (c) to the actual arrival time “9:15” of the previous flight. Estimated time. That is, the predicting unit 103 predicts that the 42nd flight will depart on time on the assumption that the time taken for the takeover is shortened to 45 minutes.

  Further, the prediction unit 103 writes the predicted departure time “10:00” in the flight information 111 indicating the flight reference date “August 1, 2011” and the flight number “42” stored in the database 101. The prediction unit 103 also includes the flight information 111 stored in the database 101, and writes the predicted time “45 minutes” in the connection information 112 indicating the previous flight “41” and the connection type “Cargo”.

  As described above, when there are a plurality of connections from the same previous flight to the same subsequent flight, the prediction unit 103 selects the longest connection time and calculates the block-out time of the subsequent flight. In other words, the prediction unit 103 obtains the predicted time of departure of the subsequent flight for each connection type (it is not necessary to actually obtain the predicted time if the required time of the connection is compared, but the estimated time is actually obtained. The latest time of the estimated departure times obtained is selected.

  In the example of FIG. 5, the database 101 shows the flight reference date “August 1, 2011”, flight number “31”, scheduled arrival time “9:00”, and actual arrival time “9:15”. It is assumed that information 111 is stored. In addition, the database 101 stores the operation reference date “August 1, 2011”, the flight number “32”, the planned departure time “10:00”, the planned arrival time “11:00”, and the actual departure time “none”. , And the connection information 112 indicating the previous flight “31”, the connection type “Ship”, the standard time “50 minutes”, and the planned time “60 minutes” is stored. Further, it is assumed that the database 101 stores connection information 112 indicating the connection type “Crew” and the standard time “30 minutes” with the same connection information 112, flight information 111, previous flight, and planned time. Further, it is assumed that the database 101 stores connection information 112 indicating the connection type “CA” and the standard time “40 minutes” with the same connection information 112, flight information 111, previous flight, and planned time.

  For this reason, the prediction unit 103 determines whether the 31st flight actually operated is the previous flight and the 32nd flight that has not been operated yet is the later flight, “Ship”, “Crew”, “CA”. Extract one connection. With these connections, the arrival of the previous flight is delayed by 15 minutes, so if the time taken to take over the equipment, crew and CA remains at the planned time, the departure of the subsequent flight will also be delayed by 15 minutes. At this time, the time satisfying the above conditions (a) to (c) is “50 minutes”, “45 minutes”, and “45 minutes” in the order of “Ship”, “Crew”, and “CA” connections. . Therefore, the prediction unit 103 adds “10:05”, “10:00”, and “10:00” obtained by adding these times to the actual arrival time “9:15” of the previous flight and predicts the departure of the subsequent flight. Candidate for time. Then, the prediction unit 103 sets “10:05” which is the latest time among these candidates as the predicted time of departure of the subsequent flight. That is, the prediction unit 103 predicts that the 32nd flight departs with a delay of 5 minutes on the assumption that the time taken for the takeover is shortened to 50 minutes.

  Further, the prediction unit 103 writes the predicted departure time “10:05” in the flight information 111 indicating the flight reference date “August 1, 2011” and the flight number “32” stored in the database 101. Further, the prediction unit 103 includes the flight information 111 stored in the database 101, indicates a common previous flight “31”, and connection information 112 indicating connection types “Ship”, “Crew”, and “CA”, respectively. The estimated time “50 minutes” is written.

  As described above, the connection to the subsequent flight may be not only from one certain flight but also from a plurality of flights. In such a case, the prediction unit 103 selects the latest one from the time obtained by adding the required time for connection to the block-in time of the previous flight, and sets it as the block-out time of the subsequent flight. That is, the prediction unit 103 obtains the predicted time of departure of the subsequent flight for each previous flight, and selects the latest time from the calculated predicted time of departure.

  In the example of FIG. 5, the database 101 shows the flight reference date “August 1, 2011”, the flight number “11”, the scheduled arrival time “9:00”, and the actual arrival time “9:15”. It is assumed that information 111 is stored. Further, the database 101 stores flight information 111 indicating the operation reference date “August 1, 2011”, the flight number “21”, the planned arrival time “8:45”, and the actual arrival time “9:30”. Suppose you are. In addition, the database 101 stores the operation reference date “August 1, 2011”, the flight number “22”, the planned departure time “10:00”, the planned arrival time “11:00”, and the actual departure time “none”. , And the connection information 112 indicating the previous flight “11”, the connection type “CA”, the standard time “40 minutes”, and the planned time “60 minutes” is stored. Further, the database 101 stores connection information 112 indicating that the connection information 112 and the flight information 111 are the same, indicating the previous flight “21”, the connection type “Ship”, the standard time “50 minutes”, and the planned time “75 minutes”. Suppose you are. Further, the database 101 stores the connection information 112 indicating that the connection information 112 is the same as the flight information 111 and indicates the previous flight “21”, the connection type “Crew”, the standard time “30 minutes”, and the planned time “75 minutes”. Suppose you are.

  For this reason, the prediction unit 103 is connected to the connection of “CA” in which the 11th flight actually operated is the previous flight, and the 22nd flight that has not been operated yet is the subsequent flight, Two connections of “Ship” and “Crew”, in which the 21st flight is the previous flight and the 22nd flight that has not yet been operated, is extracted. In the “CA” connection between flight 11 and flight 22, the arrival of the previous flight is delayed by 15 minutes, so if the time taken to take over the CA remains at the planned time, the departure of the subsequent flight will also be 15 minutes It will be late. At this time, the time that satisfies the conditions (a) to (c) described above is “45 minutes”. Therefore, the prediction unit 103 sets “10:00” obtained by adding this time to the actual time “9:15” of the arrival of the previous flight as a candidate for the predicted time of departure of the subsequent flight. In addition, in the “Ship” and “Crew” connections between Flight 21 and Flight 22, the arrival time of the previous flight is delayed by 45 minutes, so the time taken to take over the equipment and crew remains at the planned time. The departure of later flights is also delayed by 45 minutes. At this time, the time satisfying the above-described conditions (a) to (c) is “50 minutes” and “30 minutes” in the order of “Ship” and “Crew” connections. Therefore, the prediction unit 103 sets “10:20” and “10:00” obtained by adding these times to the actual arrival time “9:30” of the previous flight as candidates for the predicted departure time of the subsequent flight. Then, the prediction unit 103 sets “10:20”, which is the latest time among the three candidates, as the predicted time of departure of the subsequent flight. In this case, in the connection of “CA” connecting the 11th flight and the 22nd flight, since the subsequent flight can start according to the predicted time without reducing the time taken for the takeover, the prediction unit 103 reduces the time. (Even if it is necessary to shorten the time, it is desirable to reduce the minimum required so that the later flight can depart at the predicted time). That is, the prediction unit 103 is based on the assumption that the time taken from the 11th flight to the 22nd flight is not shortened and the time taken from the 21st flight to the 22nd flight is shortened to 50 minutes. We predict that flight 22 will depart with a delay of 20 minutes.

  Further, the prediction unit 103 writes the predicted departure time “10:20” in the flight information 111 indicating the flight reference date “August 1, 2011” and the flight number “22” stored in the database 101. In addition, the prediction unit 103 includes the flight information 111 stored in the database 101, indicates the common previous flight “21”, and stores the predicted time in the connection information 112 indicating the connection types “Ship” and “Crew”, respectively. Write “50 minutes”. Note that the prediction unit 103 includes the flight information 111 stored in the database 101, and the connection information 112 indicating the previous flight “11” and the connection type “CA” includes the actual time of arrival of the previous flight and the subsequent flight. The predicted time “65 minutes” corresponding to the difference from the predicted departure time may be written, the same predicted time “60 minutes” as the planned time may be written, or writing of the predicted time may be omitted.

  In step S103, next, the prediction unit 103 predicts and calculates the flight predicted by the flight plan time or the latest flight plan (plan that takes into account the weather, the congestion of the airport, etc.) at the blockout time of the post flight. The processing device 191 obtains the block-in time (predicted arrival time) of the subsequent flight by adding the time (operation time). Then, the prediction unit 103 writes the calculated block-in time of the subsequent flight in the flight information 111 of the database 101.

  In the example of FIG. 5, as described above, the database 101 stores the flight information 111 indicating the operation reference date “August 1, 2011”, the flight number “42”, and the estimated departure time “10:00”. It will be. Further, the database 101 stores flight information 111 indicating the operation reference date “August 1, 2011”, the flight number “32”, and the estimated departure time “10:05”. Further, the database 101 stores flight information 111 indicating the operation reference date “August 1, 2011”, the flight number “22”, and the estimated departure time “10:20”.

  Therefore, the prediction unit 103 uses the difference “60 minutes” between the planned time “10:00” for the departure of the 42nd flight and the planned time “11:00” for the arrival of the 42nd flight as “10: “11:00” added to “00” is set as the predicted arrival time of the 42nd flight. That is, the prediction unit 103 predicts that the 42nd flight will arrive on time. Further, the prediction unit 103 calculates the difference “60 minutes” between the planned time “10:00” for the departure of the 32nd flight and the scheduled time “11:00” for the arrival of the 32nd flight “10:05”. "11:05" added to "is used as the predicted arrival time of the 32nd flight. That is, the prediction unit 103 predicts that the 32nd flight will arrive with a delay of 5 minutes. Further, the prediction unit 103 calculates the difference “60 minutes” between the planned time “10:00” of departure of the 22nd flight and the planned time “11:00” of arrival of the 22nd flight “10:20 "11:20" added to "is set as the predicted arrival time of the 22nd flight. That is, the prediction unit 103 predicts that the 22nd flight will arrive with a delay of 20 minutes. As described above, when predicting the arrival time of the subsequent flight, the prediction unit 103 reduces the time required for the time (for example, flight time) from the departure to the arrival of the subsequent flight. The time (for example, a time predicted by incorporating the flight time expected by the latest flight plan) may be used as the predicted arrival time of the subsequent flight.

  Further, the prediction unit 103 writes the predicted arrival time “11:00” in the flight information 111 indicating the flight reference date “August 1, 2011” and the flight number “42” stored in the database 101. The prediction unit 103 also writes the predicted arrival time “11:05” in the flight information 111 indicating the flight reference date “August 1, 2011” and the flight number “32” stored in the database 101. The prediction unit 103 also writes the predicted arrival time “11:20” in the flight information 111 indicating the flight reference date “August 1, 2011” and the flight number “22” stored in the database 101.

  As described above, when the database 101 stores the actual arrival times of the 11th flight, the 21st flight, the 31st flight, and the 41st flight, the prediction unit 103 first connects only to these flights. The estimated departure and arrival times of the 22nd, 32nd and 42nd flights can be obtained. Although description of a specific example is omitted, the prediction unit 103 can also obtain the predicted departure and arrival times of the twelfth flight that is connected to only the eleventh flight. As a result, the database 101 stores the predicted arrival times of the twelfth flight, the twenty-second flight, the thirty-second flight, and the twenty-fourth flight. Therefore, the prediction unit 103 is next connected only to these flights. The predicted departure and arrival times of the 13th, 23rd and 33rd flights can be obtained.

  Note that the order in which the prediction unit 103 obtains the predicted departure and arrival times of each flight is arbitrary.

  For example, the prediction unit 103 can perform the delay propagation prediction calculation in the following procedure. First, the prediction unit 103 defines a set of flights or first flights that have an arrival actual time or an estimated time (which may be regarded as equivalent to the actual time), that is, a set of flights excluded from the prediction calculation target as O (initial set). To do. Then, the prediction unit 103 obtains all predicted times of departure and arrival of flights that are connected to only the flights included in the set O. Let X be the set of flights for which the predicted time is obtained at this time. Next, the prediction unit 103 obtains all predicted departure and arrival times of flights that are connected to only the flights included in the union of the set O and the set X. Let Y be the set of flights for which the predicted time is obtained at this time. Next, the prediction unit 103 obtains all predicted departure and arrival times of flights that are connected to only the flights included in the union of the set O, the set X, and the set Y. Let Z be the set of flights for which the predicted time is obtained at this time. Next, the prediction unit 103 obtains all predicted times of departure and arrival of flights that are connected to only the flights included in the union of the set O, the set X, the set Y, and the set Z. Thereafter, the prediction unit 103 similarly performs prediction calculation.

  With the above procedure, the prediction unit 103 obtains a block-out time and a block-in time for all flights that require prediction calculation. The block-out time and block-in time obtained in this way, and the block-out time and block-in time of a flight or the like having the actual value or the expected value first excluded by the prediction unit 103 are the final delayed propagation prediction. The result of the calculation.

  In step S104 of FIG. 3, the operation management support system 100 detects a delay cluster (performs clustering calculation). Details of step S104 will be described below.

  In step S104, first, the determination unit 104 detects the delay cluster by the processing device 191 based on the result of the delay propagation prediction calculation.

  Specifically, the determination unit 104 calculates the difference between the predicted time of the block-in and the planned time (delay from the planned time) for the flight that has been predicted in step S103, that is, the flight that has a predicted value in the block-in. In addition to checking, if there is a predicted value for blockout, the difference between the blockout predicted time and the planned time (delay from the planned time) is checked.

  If there is a connection between the previous flight A and the later flight B and the arrival of flight A and the departure of flight B are both delayed, the determination unit 104 determines that there is a dependency on the delay. Assume that Flight A and Flight B belong to the same set. If the A flight and the B flight belong to the same set, and the B flight and the C flight belong to the same set, the determination unit 104 determines that the A flight and the C flight belong to the same set. Note that the determination unit 104 blocks the B flight even when there is a connection between the A flight which is the previous flight and the B flight which is the subsequent flight, and the arrival of the A flight and the departure of the B flight are both delayed. If there is a record value in the out, flight A may be excluded from the target. That is, the determination unit 104 may exclude the previous flight from the target when the subsequent flight has already started (for example, in flight). However, the determination unit 104 excludes a previous flight having a plurality of subsequent flights from the target only when all the subsequent flights have already departed (for example, in flight). For example, the determination unit 104 has a connection not only between the previous flight A and the subsequent flight B, but also between the previous flight A and the subsequent flight D, If flight arrival and departure of flights B and D are both delayed, even if there is a track record value for flight B but there is no track record value for flight D, flight A is the target Do not exclude. On the other hand, if there are actual values in the block-out of both the B flight and the D flight, the A flight may be excluded from the target.

  The determination unit 104 sets the obtained set as a delay cluster as a result of performing the above check up to the last flight of the day. Note that the determination unit 104 determines that there is no connection between a flight belonging to the set M and a flight belonging to the set N in which the arrival of the previous flight is delayed and the departure of the subsequent flight is delayed. The sets M and N are determined to be different delay clusters.

  FIG. 6 is a diagram illustrating an example of the delay cluster in the example of FIG.

  In FIG. 6, a range surrounded by a dotted line represents a delay cluster. The actual time or predicted time of departure is shown at the upper left of the block representing each flight, and the planned time of departure (in parentheses) is shown above it. The actual arrival time or predicted time is shown at the upper right of the block representing each flight, and the planned arrival time (in parentheses) is shown above it. As described above, it is assumed here that the 11th flight, 21st flight, 31st flight, and 41st flight have arrived, and the other flights are before departure.

Assume that a delayed flight has occurred as follows.
(1) Flight 11: The actual arrival time is “9:15” which is 15 minutes later than the planned time “9:00”.
(2) Flight 21: The actual arrival time is “9:30” 45 minutes later than the planned time “8:45”.
(3) Flight 31: The actual arrival time is “9:15”, which is 15 minutes later than the planned time “9:00”.
(4) Flight 41: The actual arrival time is “9:15” which is 15 minutes later than the planned time “9:00”.

It is assumed that the predicted departure and arrival times of the flight before departure are obtained as follows by the delayed propagation prediction calculation in step S103.
(5) Flight 12: Due to the reduction of the planned time “70 minutes” to “55 minutes” in all connections from Flight 11, the estimated departure time becomes “10:10”, which is the same as the planned time. (The effect of the delay of the 11th flight is not affected). The predicted arrival time is “11:10”, which is the same as the planned time.
(6) Flight 13: The estimated time of departure is “13:00”, which is the same as the planned time, without reducing the planned time “110 minutes” in all connections from the 12th flight. Also, the predicted arrival time is “14:00” which is the same as the planned time.
(7) Flight 22: No matter how much the planned time “60 minutes” is shortened in the connection from Flight 11, the planned time “75 minutes” is reduced to “50 minutes” in the “Ship” connection from Flight 21 However, since it can only be shortened, the predicted departure time is “10:20”, which is 20 minutes later than the planned time “10:00” (departure is delayed due to the delay of the 11th flight and the 21st flight) . In addition, the predicted arrival time is “11:20”, which is 20 minutes later than the planned time “11:00” (the arrival is also delayed due to the delay of the 21st flight).
(8) Flight 23: Since the planned time “60 minutes” can only be reduced to “50 minutes” in the “Ship” connection from Flight 22, the predicted departure time is 10 minutes from the planned time “12:00”. Slow “12:10” (departure is delayed due to delay of flight 22) In addition, the predicted arrival time is “13:10”, which is 10 minutes later than the planned time “13:00” (the arrival is delayed due to the delay of the 22nd flight).
(9) Flight 32: Since the planned time “60 minutes” can only be reduced to “50 minutes” in the “Ship” connection from Flight 31, the estimated departure time is 5 minutes from the planned time “10:00”. Slow “10:05” (departure is delayed due to delay of flight 31). In addition, the predicted arrival time is “11:05” which is 5 minutes later than the planned time “11:00” (the arrival is delayed due to the delay of the 31st flight).
(10) Flight 33: Planned time “90 minutes” is shortened to “85 minutes” in all connections from flight 32, so the estimated departure time becomes “12:30”, the same as the planned time. (Influence of delay of flight 32) Also, the predicted arrival time is “14:00” which is the same as the planned time.
(11) Flight 42: Due to the shortened planned time “60 minutes” to “45 minutes” in the connection from flight 41, the estimated departure time is “10:00”, the same as the planned time. (Influence of delay of flight 41) The predicted arrival time is “11:00”, which is the same as the planned time.

  From the result of the above-described delayed propagation prediction calculation, the determination unit 104 determines that the actual time or predicted time of arrival of the previous flight is delayed from the planned time of arrival of the previous flight, and the predicted time of departure of the subsequent flight is the later flight. Extract connections that are behind the scheduled departure time. Specifically, the determination unit 104 connects the 11th flight to the 22nd flight, the 21st flight to the 22nd flight, the 22nd flight to the 23rd flight, the 31st flight to the 32nd flight. Extract connections to. Then, the determination unit 104 determines the grouping of the 11th flight, the 21st flight, the 22nd flight, the 23rd flight, the 31st flight, and the 32nd flight that are the previous flight or the subsequent flight of the extracted connection. Perform based on criteria. That is, the determination unit 104 performs the 11th flight, the 21st flight, the 22nd flight, the 23rd flight so that the flights to be taken over belong to the same delay cluster and each flight belongs to only one delay cluster. The 31st and 32nd flights are grouped. As a result, the determination unit 104 detects the delay cluster C1 including the 11th flight, the 21st flight, the 22nd flight, and the 23rd flight. In addition, the determination unit 104 detects a delay cluster C2 including the 31st flight and the 32nd flight. That is, the determination unit 104 determines that a delay occurs in the 22nd flight due to the influence of the delay between the 11th flight and the 21st flight, and a delay occurs in the 23rd flight due to the influence of this delay. Further, the determination unit 104 determines that a delay occurs in the 32nd flight due to the influence of the delay in the 31st flight.

  In the above example, the difference between the planned time of departure of the subsequent flight and the planned time of arrival of the previous flight is calculated as the planned time (that is, the planned time = the planned time of departure of the subsequent flight−the previous flight). However, it is also possible to set an individual planned time for each connection.

  In the above example, when the determination unit 104 extracts a connection, it is one of the criteria whether the actual time or predicted time of arrival of the previous flight is delayed from the planned time of arrival of the previous flight. It may be based on whether another service time of the previous flight is delayed from the planned time. Similarly, in the above example, when the determination unit 104 extracts a connection, one of the criteria is whether the predicted time of departure of the subsequent flight is behind the planned time of departure of the subsequent flight. It may be based on whether another operation time is delayed from the planned time. For example, the determination unit 104 determines that the actual time or predicted time of arrival of the previous flight is delayed from the planned time of arrival of the previous flight, and the predicted time of arrival of the subsequent flight is delayed from the planned time of arrival of the subsequent flight. Existing connections may be extracted. Alternatively, the determination unit 104 determines that the actual time or predicted time of departure of the previous flight is delayed from the planned time of departure of the previous flight, and the predicted time of departure of the subsequent flight is delayed from the planned time of departure of the subsequent flight. Existing connections may be extracted.

  In step S <b> 104, the output unit 105 then creates a delay propagation diagram by the processing device 191 and displays the screen on the output device 194.

Specifically, the output unit 105 generates and displays a list of delay clusters detected by the determination unit 104. At this time, for example, the output unit 105 prioritizes the delay clusters based on the following criteria.
(1) Based on the passenger information and cargo information in the connection information 112 (flight information 111), the number of important flights is specified by identifying important flights in business such as a large number of connecting passengers or carrying important cargo. Set higher priority for delay clusters.
(2) Based on the operation restrictions of the connection information 112 (flight information 111), a higher priority is set for a delay cluster that has more flights that violate operation restrictions such as runway use restrictions and landing time restrictions.
(3) A delay cluster with a shorter grace time until the actual delay (the difference between the predicted time of the flight with the earliest predicted time and the current time among the flights included in the delay cluster) is given a higher priority. Set high.
(4) A higher priority is set for a delay cluster having a larger total number of flights.
(5) A higher priority is set for a delay cluster having a longer average delay time and maximum delay time.

  FIG. 7 is a diagram illustrating an example of a list of delay clusters in the example of FIG.

  In FIG. 7, the list of delay clusters includes items (columns) such as delay clusters, priorities, flights, departure delays, and arrival delays. The delay cluster identifier is displayed in the delay cluster item. In the priority item, the priority determined by the output unit 105 for the corresponding delay cluster is displayed. In the flight item, the flight number of the flight constituting the corresponding delay cluster is displayed. The departure delay item displays the departure delay time of the corresponding flight. In the arrival delay item, the arrival delay time of the corresponding flight is displayed.

  When displaying the list of delay clusters for the first time, the output unit 105 groups the delay cluster list for each delay cluster, and sorts and displays them according to priority items. The input unit 102 may accept an operation for designating which item to sort by the user. In this case, the output unit 105 cancels the grouping for each delay cluster as necessary, and displays the list of delay clusters sorted by the item designated by the user.

  The input unit 102 receives an operation for selecting one delay cluster included in the list of delay clusters on the screen from the user. The output unit 105 generates and displays a delay propagation diagram of the delay cluster selected by the user.

  Instead of displaying the list of delay clusters, the output unit 105 may generate delay propagation diagrams for all delay clusters and display them on the same screen (for example, displaying a diagram as shown in FIG. 6). May be) However, in this case, it should be noted that the time required for processing such as display of the delay propagation diagram becomes relatively long, and that the appearance of the delay propagation diagram of each delay cluster becomes relatively small.

  FIG. 8 is a diagram illustrating an example of a delay propagation diagram in the example of FIG.

In FIG. 8, the delay propagation diagram D1 corresponds to the delay cluster C1 of FIG. The delay propagation diagram D1 graphically represents the contents of the delay cluster C1 so that the delay and its influence can be grasped at a glance. For example, the following contents are displayed.
(1) Flight: Similar to the connection diagram, it is represented by a rectangular block. The flight number is displayed in the block. The block-out time (departure actual time, predicted time if there is no actual time, planned time if there is no first flight) is displayed on the upper left of the block. The block-in time (actual arrival time, estimated time if there is no actual time, predicted time if there is no estimated time) is displayed on the upper right of the block. Blocks will be highlighted for flights that have operational impact such as runway use restrictions and landing time restrictions, such as flights that have a significant impact on delays and are important for business. In FIG. 8, since the predicted time of arrival of the 23rd flight has passed the landing time limit, the 23rd flight is highlighted.
(2) Connection: Similar to the connection diagram, it is represented by an arrow line. In FIG. 8, a label indicating the connection type is added to the line, but this is not essential. A color indicating the connection type may be set for the line, or a thickness corresponding to the time or delay amount set for the connection may be set. Even if the line type is set according to whether the time set for the connection is the planned time, the time automatically shortened by the prediction unit 103, or the time manually reduced by the user Good. Further, when there are a plurality of connections between flights, they may be aggregated into one line. When there is a connection from a plurality of flights to one flight, it is desirable to highlight a critical connection that has a large effect on delay among the connections. The critical connection will be described later.

  In addition, detailed display of the flight information 111 by a flight specifying operation (for example, clicking a block with the mouse 903), detailed display of the connection information 112 by a connection specifying operation (for example, clicking a line with the mouse 903), It is desirable to be able to perform an identification display as to whether or not the countermeasure (time reduction) is fixed.

  The input unit 102 receives information input via a GUI (graphic user interface) for the delay propagation diagram. Specifically, the input unit 102 receives the input of the countermeasure specifying the connection, updates the delay propagation diagram reflecting the latest information input from each business system after inputting the countermeasure of the connection, and displays the effect of the countermeasure. To be able to grasp.

  For example, in the delay propagation diagram D1 of FIG. 8, the user selects the “Ship” connection from the 21st flight to the 22nd flight with the mouse 903, and further reduces “40 minutes” from the standard time “50 minutes”. When input is made using the keyboard 902, the input unit 102 accepts this input and updates the countermeasure time of the connection information 112 stored in the database 101 to “40 minutes”. In this case, the prediction unit 103 performs the delay propagation prediction operation in step S103 again within the range of the delay cluster C1, and predicts that no delay will occur on the 23rd flight. Then, the output unit 105 creates and displays a delay propagation diagram D1 'reflecting the result. Note that after the prediction unit 103 performs the delay propagation prediction operation again, the determination unit 104 may perform the delay cluster detection operation in step S104 again. In this case, the 23rd flight is not included in the delay cluster C1. Then, the output unit 105 creates and displays a delay propagation diagram (not shown) reflecting the result.

  Here, the time when the user further shortens the standard time is input as the countermeasure time. However, the user can select any countermeasure time (standard) for any connection (a connection that does not cause a delay in the later flight). It may be possible to input a time longer than the time). For example, when the user selects an arbitrary connection and inputs an arbitrary countermeasure time, the input unit 102 accepts this input and updates the countermeasure time in the connection information 112 stored in the database 101 to the input one. To do. In step S103, the prediction unit 103 predicts the operation time of the subsequent flight using the countermeasure time for the connection for which the countermeasure time is set, without using the planned time or the standard time. In other words, the prediction unit 103 determines the block-in time of the previous flight and the time required for the connection (plan time if there is no measure time or plan time, and a delay occurs if the plan time is the plan time) for the part (flight) that requires prediction calculation Calculates the block-out time (predicted departure time) of the subsequent flight from the standard time as a lower limit.

  FIG. 9 is a diagram illustrating another example of the delay propagation diagram in the example of FIG.

  In FIG. 9, the delay propagation diagram D1 is the same as the delay propagation diagram D1 of FIG.

  For example, in the delay propagation diagram D1 of FIG. 9, the user selects the “Ship” connection from the 21st flight to the 22nd flight with the mouse 903, disconnects this connection, and sets the standby (standby) equipment. When input to the 22nd flight is input by the keyboard 902 or the mouse 903, the input unit 102 updates the connection information 112 stored in the database 101 in response to the input. For example, the input unit 102 includes the flight information 111 indicating the flight reference date “August 1, 2011” and the flight number “22”, and the connection information 112 indicating the previous flight “21” and the connection type “Ship” in the database. Delete from 101. In this case, the prediction unit 103 performs the delay propagation prediction operation in step S103 again within the range of the delay cluster C1, so that no delay occurs in the 22nd flight, and no delay occurs in the 23rd flight. Predict. Then, the output unit 105 creates and displays a delay propagation diagram D1 ″ reflecting the result. Note that after the prediction unit 103 performs the operation of delay propagation prediction again, the determination unit 104 performs the delay of step S104. The cluster detection operation may be performed again, in which case the delay cluster C1 is eliminated, and the output unit 105 displays, for example, a message to that effect.

  In FIG. 3, steps S102 to S104 may be executed at regular time intervals, that is, periodically, or every time step S101 is executed, that is, the input unit 102 receives the flight information 201, It is executed every time the database 101 is updated by obtaining the latest information of at least one of flight plan information 202, GH information 203, crew work information 204, CA work information 205, passenger information 206, or cargo information 207. May be. In step S103, the prediction unit 103 redoes the delay propagation prediction every time. In step S104, based on the result of the delay propagation prediction, the determination unit 104 detects the delay cluster again. Thereby, the user can confirm the transition of the delay cluster (the state of occurrence of the delay and the influence range thereof change as time passes).

As described above, according to the present embodiment, in the schedule management of public transportation, the range affected by a certain irregularity is extracted as a lump (delay cluster), thereby improving the efficiency of the irregularity countermeasure study. The procedure is as follows.
(1) Automatically extract a delay cluster that originates from a certain irregularity from a diagram in which all connections are centrally managed and display it on the screen.
(2) Each connection has a planned time on the diagram and a minimum required standard time (standard time ≦ planned time).
(3) The delay cluster extracts only those which do not become on time but have an effect even if each connection is compressed to the standard time.
(4) While looking at it, each person in charge cooperates and examines irregular countermeasures (recovery plans) on the screen manually.
(5) When the person in charge inputs countermeasures, the delay cluster is recalculated and managed accordingly.

  According to the present embodiment, it is possible to reduce loss cost due to unexpected delay expansion and to improve punctuality. Further, according to the present embodiment, it is possible to reliably and easily identify the influence of delay by grasping the connection. In addition, according to the present embodiment, it is possible to extract the influence range of delay as a delay cluster, calculate risk information for each connection, and plan an overall optimum measure from a wide viewpoint rather than a local viewpoint. In addition, according to the present embodiment, even if the department that manages the GH at the airline is different from the department that manages the crew, CA, and passenger connection, it is easy to examine countermeasures across the departments.

  In this embodiment, aircraft operation management is supported by the operation management support system 100. However, in the present embodiment, the operation management of a transportation system having a takeover element (equipment, passengers, crew, cargo, etc.). If so, even operation management of railways, ships, automobiles, and the like can be targeted for support by the operation management support system 100. In addition, for example, it is considered that passengers, cargo, etc. are taken over between the aircraft and the railway directly connected to the airport building. It can be a support target of the support system 100.

  Further, in the present embodiment, the database 101 stores flight information 111 indicating the actual time for flights that have already been operated, but the database 101 stores temporary actual times for some of the flights that have not yet been operated. The flight information 111 shown may be stored. The provisional actual time is a simulation time given by a user or the like, and is treated in the same way as the actual time. In this case, it is possible to perform a delay propagation prediction by the prediction unit 103 and a delay cluster detection simulation by the determination unit 104. The simulation result can be easily confirmed by, for example, the delay cluster list shown in FIG. 7 or the delay propagation diagram shown in FIG.

  In the present embodiment, the database 101 stores flight information 111 indicating not only the operation time but also the departure and arrival airports (departure and arrival points) as operation contents. The prediction unit 103 uses this information. You may predict the service time of later flights. In this case, when the prediction unit 103 extracts a connection in which the actually operated flight is the previous flight and the flight that has not been operated yet is the subsequent flight, the prediction unit 103 arrives at the previous flight (actual or planned stage). Check if the airport and the departure airport (planning stage) of the later flight are the same airport. If the arrival airport of the previous flight and the departure airport of the subsequent flight are the same airport, the prediction unit 103 predicts the operation time of the subsequent flight from the actual time of the previous flight. On the other hand, if the arrival airport of the previous flight and the departure airport of the subsequent flight are not the same airport, the prediction unit 103 determines that the operation time of the subsequent flight cannot be predicted. Alternatively, if the connection type of the extracted connection is only “Ship”, “Crew”, or “CA”, the prediction unit 103 disconnects the connection, and puts the standby equipment, crew, and CA into the subsequent flight. Based on this assumption, it is predicted that the scheduled time for the later flight will be the operation time for the later flight. At this time, the prediction unit 103 may delete the connection information 112 corresponding to the connection from the database 101. When the prediction unit 103 determines that the operation time of the subsequent flight cannot be predicted, the output unit 105 displays a message or the like to that effect. In addition, when the prediction unit 103 predicts the operation time of the subsequent flight on the assumption that the standby equipment, crew, and CA are put into the subsequent flight, the output unit 105 adds a display to that effect to the delay propagation diagram. . As a result, when the landing airport of a flight in flight is changed due to bad weather, etc., the affected flight is identified and notified to the user, or the delay effect after considering standby input Can be specified.

Embodiment 2. FIG.
In the present embodiment, differences from the first embodiment will be mainly described.

  The configuration of the operation management support system 100 according to the present embodiment is the same as that of the first embodiment shown in FIG.

  In the present embodiment, the delay cluster detection operation by the determination unit 104 is different from that of the first embodiment.

  In the present embodiment, the determination unit 104 refers to the flight information 111 and the connection information 112 stored in the database 101 in the same manner as in the first embodiment, and the actual time or predicted time of the previous flight is the previous flight. A set of connections that are delayed from the planned time and whose predicted time of the subsequent flight is delayed from the planned time of the subsequent flight is extracted. Then, the determination unit 104 performs grouping so that the flights to be taken over belong to the same delay cluster and each flight belongs to only one delay cluster as a predetermined determination criterion. At this time, in the present embodiment, when the subsequent flight is common to the set of extracted connections and a plurality of connections with different previous flights are included, the flights of only critical connections belong to the same delay cluster. In this way, grouping is performed. That is, when a plurality of connections with different previous flights are connected to one subsequent flight, only the previous flight of the critical connection among the previous flights of these connections enters the same delay cluster as that one subsequent flight. The critical connection is a connection in which the operation time of the previous flight is most delayed from the limit time of the previous flight among a plurality of connections shared by the subsequent flights. The limit time of the previous flight refers to the time when the previous flight should be operated in order to operate the subsequent flight at the planned time of the subsequent flight. Specifically, in a certain connection, a time obtained by subtracting the countermeasure time or the standard time (if there is no countermeasure time) from the scheduled time of departure of the subsequent flight is the limit time of the previous flight.

  In addition, as described above, instead of setting a critical connection only for a connection in which the operation time of the previous flight is most delayed from the limit time of the previous flight, the operation time of the previous flight is changed to a critical connection. All connections that are delayed by a predetermined time or more from the limit time of the previous flight may be treated as critical connections. In this case, all connections in which the operation time of the previous flight is slightly delayed from the limit time of the previous flight may be designated as critical connections, or the operation time of the previous flight may be any time (for example, 30 minutes from the limit time of the previous flight) Only the connections that are delayed above may be critical connections.

  Hereinafter, an example of the operation of the operation management support system 100 (the operation management support method according to the present embodiment, the processing procedure of the program according to the present embodiment) is used as in the first embodiment, as shown in FIG. I will explain.

  FIG. 10 is a diagram illustrating an example of the delay cluster in the example of FIG.

  The delay occurring in the arrived flight is assumed to be the same as the example of the first embodiment shown in FIG. Further, the predicted departure and arrival times of the flights before departure obtained by the delayed propagation prediction calculation in step S103 in FIG. 3 are also the same as those in the example of the first embodiment shown in FIG.

  From the result of the delayed propagation prediction calculation, the determination unit 104 determines that the actual time or predicted time of arrival of the previous flight is behind the planned time of arrival of the previous flight, and the predicted time of departure of the subsequent flight is the departure time of the subsequent flight. The set of connections that are behind the scheduled time is extracted. Specifically, the determination unit 104 connects the 11th flight to the 22nd flight, the 21st flight to the 22nd flight, the 22nd flight to the 23rd flight, the 31st flight to the 32nd flight. Extract connections to. The determination unit 104 then performs the 11th flight, the 21st flight, the 22nd flight, and the 23rd flight so that the flights to be taken over belong to the same delay cluster and each flight belongs to only one delay cluster. The 31st and 32nd flights are grouped. Here, of the connections extracted by the determination unit 104, the connection from the 11th flight to the 22nd flight and the subsequent flight from the 21st flight to the 22nd flight are common. In the connection from the eleventh flight to the twenty-second flight, the eleventh flight from the scheduled time “9:20” of the eleventh flight obtained by subtracting the standard time “40 minutes” from the scheduled time “10:00” of the twenty-second flight, The actual flight arrival time “9:15” is 5 minutes earlier. That is, in this connection, the operation time of the previous flight is delayed by -5 minutes from the limit time of the previous flight. In the “Ship” connection from the 21st flight to the 22nd flight, the time limit “9:10” of the 21st flight is obtained by subtracting the standard time “50 minutes” from the scheduled time “10:00” of the departure of the 22nd flight. Therefore, the actual time of arrival of flight # 21 “9:30” is 20 minutes later. That is, in this connection, the operation time of the previous flight is delayed by 20 minutes from the limit time of the previous flight. In the “Crew” connection from the 21st flight to the 22nd flight, the time limit “9:30” of the 21st flight is obtained by subtracting the standard time “30 minutes” from the scheduled time “10:00” of the departure of the 22nd flight. And the actual time “9:30” of arrival of the 21st flight is the same. That is, in this connection, the operation time of the previous flight is delayed by 0 minutes from the limit time of the previous flight. Therefore, of the connection from the 11th flight to the 22nd flight and the connection from the 21st flight to the 22nd flight, the operation time of the previous flight is most delayed from the limit time of the previous flight, the 21st flight to the 22nd flight The “Ship” connection to is a critical connection. Therefore, the determination unit 104 detects a delay cluster C1 'that does not include the eleventh flight but includes only the twenty-first flight, the twenty-second flight, and the twenty-third flight. Moreover, the determination part 104 detects the delay cluster C2 comprised by the 31st flight and the 32nd flight similarly to the example of Embodiment 1 shown in FIG.

  FIG. 11 is a diagram illustrating an example of a list of delay clusters in the example of FIG.

  In FIG. 11, the configuration of the delay cluster list is the same as the example of the first embodiment shown in FIG.

  10 and 11 are compared with FIGS. 6 and 7, in FIG. 6 and FIG. 7, the eleventh flight is included in the delay cluster C1, whereas in FIG. 10 and FIG. The 11th flight is not included.

  FIG. 12 is a diagram illustrating an example of a delay propagation diagram in the example of FIG.

  In FIG. 12, the delay propagation diagram D1 corresponds to the delay cluster C1 'of FIG.

  For example, in the delay propagation diagram D1 of FIG. 12, the user selects the “Ship” connection from the 21st flight to the 22nd flight with the mouse 903, and further reduces “40 minutes” from the standard time “50 minutes”. When input is made using the keyboard 902, the input unit 102 accepts this input and updates the countermeasure time of the connection information 112 stored in the database 101 to “40 minutes”. In this case, the prediction unit 103 performs the delay propagation prediction operation in step S103 again within the range of the delay cluster C1 ', and predicts that no delay will occur on the 23rd flight. Then, the output unit 105 creates and displays a delay propagation diagram D1 'reflecting the result. Note that after the prediction unit 103 performs the delay propagation prediction operation again, the determination unit 104 may perform the delay cluster detection operation in step S104 again. In this case, the 23rd flight is not included in the delay cluster C1 '. Then, the output unit 105 creates and displays a delay propagation diagram (not shown) reflecting the result.

  According to the present embodiment, by excluding the previous flights of connections other than the critical connection from the delay cluster, the user can be surely recognized a connection having a large influence of delay. Therefore, the user can plan effective measures more quickly.

Embodiment 3 FIG.
In the present embodiment, differences from the first embodiment will be mainly described.

  The configuration of the operation management support system 100 according to the present embodiment is the same as that of the first embodiment shown in FIG.

  In the present embodiment, the delay cluster detection operation by the determination unit 104 is different from that of the first embodiment.

  In the present embodiment, the determination unit 104 refers to the flight information 111 and the connection information 112 stored in the database 101, and the actual time or predicted time of the previous flight is delayed from the planned time of the previous flight, and Then, a set of connections in which the predicted time of the subsequent flight is delayed from the planned time of the subsequent flight is extracted. Then, as a predetermined determination criterion, the determination unit 104 has a delay cluster for each flight that is not a subsequent flight in any of the connections included in the extracted connection set, and the flights to be taken over are the same delay cluster. Group so that it belongs to

  Hereinafter, an example of the operation of the operation management support system 100 (the operation management support method according to the present embodiment, the processing procedure of the program according to the present embodiment) is used as in the first embodiment, as shown in FIG. I will explain.

  FIG. 13 is a diagram illustrating an example of the delay cluster in the example of FIG.

  The delay occurring in the arrived flight is assumed to be the same as the example of the first embodiment shown in FIG. Further, the predicted departure and arrival times of the flights before departure obtained by the delayed propagation prediction calculation in step S103 in FIG. 3 are also the same as those in the example of the first embodiment shown in FIG.

  From the result of the delayed propagation prediction calculation, the determination unit 104 determines that the actual time or predicted time of arrival of the previous flight is behind the planned time of arrival of the previous flight, and the predicted time of departure of the subsequent flight is the departure time of the subsequent flight. The set of connections that are behind the scheduled time is extracted. Specifically, the determination unit 104 connects the 11th flight to the 22nd flight, the 21st flight to the 22nd flight, the 22nd flight to the 23rd flight, the 31st flight to the 32nd flight. Extract connections to. Then, the determination unit 104 determines the grouping of the 11th flight, the 21st flight, the 22nd flight, the 23rd flight, the 31st flight, and the 32nd flight that are the previous flight or the subsequent flight of the extracted connection. Perform based on criteria. That is, the determination unit 104 determines that there is a delay cluster for each flight that is not a subsequent flight in any of the connections included in the extracted connection set, and that the flights to be transferred belong to the same delay cluster. 11 flights, 21 flights, 22 flights, 23 flights, 31 flights and 32 flights are grouped. As a result, the determination unit 104 detects the delay cluster C1-1 including the 11th flight, the 22nd flight, and the 23rd flight. Further, the determination unit 104 detects the delay cluster C1-2 including the 21st flight, the 22nd flight, and the 23rd flight. In addition, the determination unit 104 detects a delay cluster C2 including the 31st flight and the 32nd flight. That is, the determination unit 104 determines that a delay occurs in the 22nd flight due to the effect of the delay of the 11th flight, and a delay occurs in the 23rd flight due to the effect of this delay. The determination unit 104 determines that a delay occurs in the 22nd flight due to the delay of the 21st flight, and a delay occurs in the 23rd flight due to the influence of the delay. Further, the determination unit 104 determines that a delay occurs in the 32nd flight due to the influence of the delay in the 31st flight.

  FIG. 14 is a diagram illustrating an example of a list of delay clusters in the example of FIG.

  In FIG. 14, the configuration of the delay cluster list is the same as that of the example of the first embodiment shown in FIG.

  13 and 14 are compared with FIGS. 6 and 7, in FIGS. 6 and 7, the 22nd and 23rd flights belong to only one delay cluster C1, and the 11th and 21st flights belong to this delay cluster C1. While both flights are included, in FIGS. 13 and 14, the 22nd flight and the 23rd flight belong to both of the two delay clusters C1-1 and C1-2, and the 2nd flight belongs to the delay cluster C1-1. Eleventh flight, 21st flight is included in the delay cluster C1-2.

  For example, the delay propagation diagram corresponding to the delay cluster C1-2 in FIG. 13 is like the delay propagation diagram D1 shown in FIG.

  According to this embodiment, when a delay occurs in one flight that has already arrived, the user can easily grasp the influence of the delay of the flight.

  In the present embodiment, critical connections are not taken into account when grouping by the determination unit 104, but as in the second embodiment, the determination unit 104 uses a common set of connections for the extracted set of connections. However, when a plurality of connections with different previous flights are included, grouping may be performed so that flights with only critical connections belong to the same delay cluster. In this case, in the example of FIG. 13, since the connection from the 11th flight to the 22nd flight is not a critical connection, the determination unit 104 determines that the delay cluster C1- is composed of the 11th flight, the 22nd flight, and the 23rd flight. 1 is not detected. That is, the determination unit 104 detects only the delay cluster C1-2 including the 21st flight, the 22nd flight, and the 23rd flight and the delay cluster C2 including the 31st flight and the 32nd flight. As a result, it is possible to make the user recognize a connection having a large influence of delay.

Embodiment 4 FIG.
In the present embodiment, differences from the first embodiment will be mainly described.

  FIG. 15 is a block diagram showing a configuration of the operation management support system 100 according to the present embodiment.

  As shown in FIG. 15, in this embodiment, the operation management support system 100 does not include the prediction unit 103 of the first embodiment shown in FIG. 1 (may be provided, but is not necessary).

  In the present embodiment, the database 101 stores flight information 111 indicating planned times and actual times (may be the provisional actual times described above) for all flights. The database 101 stores connection information 112 similar to that in the first embodiment.

  The determination unit 104 refers to the flight information 111 and the connection information 112 stored in the database 101, the actual time of the previous flight is delayed from the planned time of the previous flight, and the actual time of the subsequent flight is The set of connections that are behind the scheduled time is extracted. Then, the determination unit 104 performs grouping of the previous flight and the subsequent flight of the connection included in the extracted set of connections based on a predetermined determination criterion. For example, as in the first embodiment, the determination unit 104 sets the group so that the flights to be taken over belong to the same delay cluster and each flight belongs to only one delay cluster, as a predetermined determination criterion. Divide. In addition, as this determination criterion, the same criterion as in the second embodiment or the third embodiment may be used.

  The output unit 105 outputs a delay propagation diagram corresponding to any one delay cluster as the group information 114 as in the first embodiment.

  According to the present embodiment, the user can analyze the past occurrence of delays and how they are affected, and use the results when formulating a future operation plan.

  In the present embodiment, as in the first embodiment, the database 101 stores flight information 111 indicating the departure and arrival airports (departure and arrival points) as operation details. Based on this, when the arrival airport of the previous flight and the departure airport of the subsequent flight are not the same airport, it may be determined that the predetermined operation condition described above is not satisfied. That is, the determination unit 104 refers to the flight information 111 and the connection information 112 stored in the database 101 and extracts a set of connections in which the arrival airport of the previous flight and the departure airport of the subsequent flight are not the same airport. Also good. Alternatively, the determination unit 104 may extract a set of connections in which irregularities other than delays or changes in arrival airports have occurred. In any case, the determination unit 104 is a group of the previous flight and the subsequent flight of the connection included in the extracted set of connections based on a predetermined determination criterion appropriately determined according to the use of the operation management support system 100 or the like. Divide. As a result, it becomes easy to grasp the range affected by irregularities generated in transportation, and to analyze the influence of irregularities on subsequent flights.

  As mentioned above, although embodiment of this invention was described, you may implement in combination of 2 or more among these embodiment. Alternatively, one of these embodiments may be partially implemented. Alternatively, two or more of these embodiments may be partially combined. In addition, this invention is not limited to these embodiment, A various change is possible as needed.

  100 operation management support system, 101 database, 102 input unit, 103 prediction unit, 104 determination unit, 105 output unit, 111 flight information, 112 connection information, 113 update information, 114 group information, 191 processing device, 192 storage device, 193 Input device, 194 output device, 201 flight information, 202 flight plan information, 203 GH information, 204 crew work information, 205 CA work information, 206 passenger information, 207 cargo information, 901 LCD, 902 keyboard, 903 mouse, 904 FDD, 905 CDD, 906 printer, 911 CPU, 912 bus, 913 ROM, 914 RAM, 915 communication board, 920 HDD, 921 operating system, 922 window system, 923 program Beam group, 924 files.

Claims (13)

An operation management support system that supports operation management of a moving object in a transportation facility,
For each flight that is a unit of operation of the moving body, the flight information indicating the operation content is stored in the storage device, and at least one of the transfer elements carried by the moving body and the moving body is performed between flights. A database that stores, with a storage device, takeover information indicating a takeover source flight and a takeover destination flight for each takeover;
With reference to the flight information and takeover information stored in the database, a set of takeovers in which the operation details of the takeover source flight and the takeover destination flights do not satisfy predetermined operation conditions are extracted, A determination unit that performs grouping of a takeover source flight and a takeover destination flight included in the extracted set of takeovers by the processing device based on the determination criteria;
An operation management support system comprising: an output unit that outputs group information indicating flights constituting one arbitrary group out of flights grouped by the determination unit.   The determination unit, as the predetermined determination criterion, performs the grouping so that flights to be taken over belong to the same group and each flight belongs to only one group. 1 Operation management support system.   The determination unit has, as the predetermined determination criterion, a group exists for each flight that is not a takeover destination flight in any of the takeovers included in the extracted takeover set, and the flights to be taken over are in the same group. The operation management support system according to claim 1, wherein the grouping is performed so as to belong. The database shows the operation time as the operation content for each flight, and further stores the flight information indicating the planned time which is the time when the operation is planned in advance.
The determination unit, when the operation time of the takeover source flight is delayed from the planned time of the takeover source flight, and when the operation time of the takeover destination flight is delayed from the planned time of the takeover destination flight, the predetermined unit The operation management support system according to claim 1, wherein the operation condition is determined not to be satisfied.   In the case where a plurality of takeovers including different takeover flights are included in the extracted set of takeovers, the determination unit determines that the operation time of the takeover source flights among the plurality of takeovers is the takeover destination flights. So that only the takeover flights that are delayed by more than a predetermined time from the limit time when the takeover source flights should be operated or only the latest takeover flights belong to the same group. The operation management support system according to claim 4, wherein the grouping is performed. The database stores flight information indicating the actual operation time as the operation content for the already operated flights,
The operation management support system further includes:
With reference to the flight information and takeover information stored in the database, the takeover in which the actually operated flight is the takeover source flight and the flight not yet operated is the takeover destination flight is extracted. The extracted operation time is the predicted operation time for the flight whose predicted operation time is predicted each time the processing time is predicted by the processing device from the actual time of the transfer source flight, and the operation time is predicted. The flight information indicating the predicted time as the operation content is stored in the database, and if there is a takeover with the flight whose operation time is predicted as the takeover flight, the takeover is extracted, and the extracted takeover is 6. The operation management support system according to claim 4 or 5, further comprising a prediction unit that predicts the operation time of the takeover flight from the prediction time by a processing device. For each takeover, the database further stores takeover information indicating a standard time taken for takeover,
For the extracted takeover, if the actual time or predicted time of the takeover source flight is not delayed from the plan time of the takeover source flight, the plan time of the takeover destination flight becomes the operation time of the takeover destination flight. If the actual time or predicted time of the flight of the takeover source is delayed from the planned time of the flight of the takeover source, an arbitrary time equal to or greater than the standard time is added to the actual time or predicted time of the flight of the takeover source. The operation management support system according to claim 6, wherein the time after the planned time of the takeover flight is predicted to be the operation time of the takeover flight. For each takeover, the database further stores takeover information indicating the type of takeover element,
The prediction unit predicts the operation time of the takeover flight for each takeover when the takeover flight and the takeover flight are common, and a plurality of takeovers with different types of takeover elements are extracted. The operation management support system according to claim 6 or 7, wherein the latest operation time among the predicted operation times is set as a prediction time of a destination flight.   9. The operation management support system according to claim 8, wherein the type of the takeover element includes at least one of a passenger, a passenger, and a cargo carried by the mobile body.   The forecasting unit predicts the operation time of the destination flight for each extracted takeover when extracting a plurality of takeovers in which the destination flight is common and the takeover source flights are different, and among the predicted operation time 10. The operation management support system according to any one of claims 6 to 9, wherein the latest time is set as a predicted time of a takeover destination flight. The operation management support system further includes:
An input unit that receives input of update information for updating flight information or transfer information stored in the database by an input device, and updates the flight information or transfer information stored in the database,
The determination unit refers to the flight information and takeover information stored in the database each time the flight information or takeover information stored in the database is updated by the input unit, and newly sets a takeover set. The operation management support system according to any one of claims 1 to 10, wherein the operation management support system according to any one of claims 1 to 10, wherein the operation is performed and grouping is performed on a takeover source flight and a takeover destination flight included in the extracted set of takeovers.   The output unit represents, as the group information, a stool constituting the arbitrary one group by a figure, and within the arbitrary one group, for each takeover included in the set of takeovers extracted by the determination unit The operation management support system according to any one of claims 1 to 11, further comprising outputting a diagram representing a relationship between a takeover source flight and a takeover destination flight with lines between figures. A program that supports the operation management of moving objects in transportation,
For each flight that is a unit of operation of the moving body, the flight information indicating the operation content is stored in the storage device, and at least one of the transfer elements carried by the moving body and the moving body is performed between flights. For each takeover, a computer having a database for storing takeover information indicating a takeover source flight and a takeover destination flight by a storage device,
With reference to the flight information and takeover information stored in the database, a set of takeovers in which the operation details of the takeover source flight and the takeover destination flights do not satisfy predetermined operation conditions are extracted, A determination unit that performs grouping of a takeover source flight and a takeover destination flight included in the extracted set of takeovers by the processing device based on the determination criteria;
The program for functioning as an output part which outputs the group information which shows the flights which comprise arbitrary one group among the flights by which the determination part performed the grouping with an output device.

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