CN115438898A

CN115438898A - First object distribution method and device, storage medium and electronic device

Info

Publication number: CN115438898A
Application number: CN202210577501.0A
Authority: CN
Inventors: 田伟云; 洪建兵; 付蓉; 赖峰; 余鹏
Original assignee: Zhuhai Unitech Power Technology Co Ltd
Current assignee: Zhuhai Unitech Power Technology Co Ltd
Priority date: 2022-05-25
Filing date: 2022-05-25
Publication date: 2022-12-06
Anticipated expiration: 2042-05-25
Also published as: WO2023226324A1; CN115438898B

Abstract

The invention provides a method and a device for distributing a first object, a storage medium and an electronic device, wherein the method comprises the following steps: dividing each train line according to the transfer stations on each train line to obtain a plurality of train tasks, wherein any one train task in the plurality of train tasks is used for indicating a running task of a train from one transfer station to another transfer station; determining the state information of each train task according to the starting and stopping station of each train task in the plurality of train tasks; allocating a first object for the train task with different state information based on a first algorithm and a second algorithm, wherein the first algorithm comprises the following steps: the second algorithm at least comprises one of the following algorithms: a greedy grid algorithm, a tabu search algorithm and a simulated annealing algorithm solve the problem that distribution results are not ideal (for example, tasks are not balanced enough, the number of drivers is large, and calculation time is large) when a single algorithm is used for distributing a first object in the related technology.

Description

First object distribution method and device, storage medium and electronic device

Technical Field

The invention relates to the field of crew scheduling assignment management, in particular to a first object assignment method, a first object assignment device, a storage medium and an electronic device.

Background

The crew scheduling plan refers to a work plan for allocating drivers (i.e., first objects) to different train tasks based on a train operation diagram. At present, a crew scheduling plan is generally completed by field workers with abundant working experience, and the manual scheduling method is time-consuming, labor-consuming and inaccurate, and can affect the operation efficiency and the service level of a train.

In order to overcome the above problems, in the related art, the drivers are generally assigned using a shift schedule automatically generated by an algorithm, and commonly used algorithms include an optimization algorithm and a heuristic algorithm, wherein the optimization algorithm includes a column generation algorithm and a branch definition algorithm. At present, the optimization algorithm can generate a shift schedule in a mode of solving a global optimal solution only under the condition of small data volume, and when the data volume is large, the optimization algorithm needs to consume huge time overhead to obtain the shift schedule, so that the time cost for generating the shift schedule is greatly increased, and the process of allocating drivers consumes too long time. Although the heuristic algorithm does not need to consume a large amount of time to generate the shift scheduling plan, the 'neighborhood' range of the heuristic algorithm is extremely large and discontinuous, when the shift scheduling plan is generated, the phenomena of algorithm unconvergence or algorithm precocity and the like are easily caused, the result which is often greatly deviated from the global optimal solution is obtained, and the allocation result of a driver is not ideal (for example, the task is not balanced enough, the number of drivers is large, and the calculation time is long). Because each algorithm has certain inevitable algorithm defects, the allocation result of the method for allocating drivers by using only a single algorithm is not ideal enough, and the operation efficiency and the service level of the train cannot be really improved.

Therefore, an effective technical solution has not been proposed for the problem in the related art that the result of the distribution is not ideal (for example, the task is not balanced enough, the number of drivers is large, and the calculation time is large) when the first object is distributed by using a single algorithm.

Disclosure of Invention

Embodiments of the present invention provide a method, an apparatus, a storage medium, and an electronic apparatus for distributing a first object, so as to at least solve a problem in the related art that a result of distribution is not ideal (e.g., a number of drivers is large) when a single algorithm is used to distribute the first object.

According to an embodiment of the present invention, there is provided a method of allocating a first object, including: dividing each train line according to the transfer stations on each train line to obtain a plurality of train tasks, wherein any one train task in the plurality of train tasks is used for indicating a running task of a train from one transfer station to another transfer station; determining the state information of each train task according to the starting and stopping station of each train task in the plurality of train tasks, wherein the state information comprises one of the following: returning to the field, exiting from the field and outputting, and setting; allocating a first object for the train task with different state information based on a first algorithm and a second algorithm, wherein the first algorithm at least comprises one of the following steps: a greedy forward algorithm and a greedy reverse algorithm, wherein the second algorithm at least comprises one of the following algorithms: greedy grid algorithm, tabu search algorithm, simulated annealing algorithm.

In an alternative exemplary embodiment, assigning a first object to a train task with different status information based on a first algorithm and a second algorithm includes: determining a plurality of preset algorithm flows and an algorithm type corresponding to each algorithm flow in the plurality of algorithm flows, wherein the algorithm type at least comprises one of the following steps: the first algorithm, the second algorithm; calculating a plurality of local solutions corresponding to each algorithm flow according to the algorithm type corresponding to each algorithm flow, and combining the local solutions to obtain a target solution corresponding to each algorithm flow, wherein the target solution represents a result of distributing a first object for the train tasks with different state information under each algorithm flow; and determining a preset number of target solutions from the plurality of target solutions corresponding to the plurality of algorithm flows, and distributing a first object for the train tasks with different state information according to the preset number of target solutions.

In an alternative exemplary embodiment, before allocating the first object to the train task with different status information based on the first algorithm and the second algorithm, the method further comprises: obtaining scheduling constraint conditions of the plurality of train tasks, wherein the scheduling constraint conditions comprise: a shift dividing parameter, a dining or rest parameter, and a shift workload parameter; generating a scheduling task based on the shift dividing parameter, the dining or rest parameter, and the shift workload parameter, wherein the shift comprises a first shift, a second shift, and a third shift, and the task start time or the task end time of the train task in the first shift, the second shift, and the third shift are different.

In an alternative exemplary embodiment, the assigning the first object to the train task of different status information based on the first algorithm and the second algorithm comprises: calculating the state information of the first train task of the first object in the first shift according to the greedy reverse order algorithm in a reverse order mode, wherein the state information is a first distribution result of the train tasks of the return section; calculating a second distribution result of the train tasks which are not distributed in the first shift in a positive sequence mode according to the greedy positive sequence algorithm; calculating the state information of the first train task of the first object in the second shift and the third shift according to the greedy positive sequence algorithm, wherein the state information is a third distribution result of the train tasks which are out of the field and out of the section; calculating the state information of the first train task of the first object in the second shift and the third shift according to the greedy reverse order algorithm in a reverse order mode, wherein the state information is the fourth distribution result of the train tasks of the return section; calculating a fifth distribution result of the unallocated train tasks in the second shift and the third shift according to the second algorithm; and determining the sum of the first distribution result, the second distribution result, the third distribution result, the fourth distribution result and the fifth distribution result as the distribution result of the first object distributed to the train tasks with different state information.

In an optional exemplary embodiment, the calculating the state information of the first train task of the first object in the first shift according to the greedy reverse ordering algorithm in a reverse order is a first distribution result of train tasks of a return field and a return segment comprises: sequencing all the train tasks in a reverse order according to the task ending time to obtain a first train task list after sequencing in the reverse order; and distributing the first train task of the first object in the first train task list to the first shift according to the greedy reverse order algorithm, wherein the state information of the first train task of the first object is that the first object is distributed to the train tasks in the return section of the return field, and a first distribution result is obtained, wherein the first distribution result comprises a first task amount distributed to the first object.

In an alternative exemplary embodiment, the forward-sequence calculating the second distribution result of the train tasks which are not distributed in the first shift according to the greedy forward-sequence algorithm comprises: performing positive sequence sequencing on the unallocated train tasks according to the task starting time to obtain a positive sequence sequenced second train task list; and distributing the first object to the train task of which the first train task belongs to the first shift in the second train task list according to the greedy positive sequence algorithm to obtain a second distribution result, wherein the second distribution result comprises a second task amount distributed to the first object.

In an optional exemplary embodiment, the forward-sequence calculating according to the greedy forward-sequence algorithm that the state information of the first train task of the first object in the second shift and the third shift is the third distribution result of the train task which is out of field and out of section comprises: performing positive sequence sequencing on the unallocated train tasks according to the task starting time to obtain a third train task list subjected to positive sequence sequencing; and allocating the first train task of the first object in the third train task list to the second shift and the third shift according to the greedy positive sequence algorithm, wherein the state information of the first train task of the first object is that the first object is allocated to the train task of the departure and departure section, and a third allocation result is obtained, wherein the third allocation result comprises a third task amount allocated to the first object.

In an optional exemplary embodiment, the calculating the state information of the first train task of the first object in the second shift and the third shift in reverse order according to the greedy reverse order algorithm is a fourth distribution result of the train tasks of the return section comprises: sequencing the unallocated train tasks in a reverse order according to the task ending time to obtain a fourth train task list after the reverse order is sequenced; and distributing the first train task of the first object in the fourth train task list to the second shift and the third shift according to the greedy reverse order algorithm, wherein the state information of the first train task of the first object is that the train task of the return section distributes the first object to obtain a fourth distribution result, and the fourth distribution result comprises a fourth task amount distributed to the first object.

In an alternative exemplary embodiment, the fifth assignment result of the unassigned train tasks in the second shift and the third shift according to the second algorithm comprises: performing positive sequence sequencing on the unallocated train tasks according to the starting time to obtain a fifth train task list subjected to positive sequence sequencing; and allocating the first object to the train tasks of the second class and the third class of the first object in the fifth train task list according to the second algorithm to obtain a fifth allocation result, wherein the fifth allocation result comprises a fifth task amount allocated to the first object.

In an alternative exemplary embodiment, after allocating the first object to the train task with different status information based on the first algorithm and the second algorithm, the method includes: evaluating the distribution result of the first object by using an evaluation function MinF to obtain an evaluation function value, wherein the evaluation function MinF is as follows:

wherein f is ₁ (j)、f ₂ (j)、f ₃ (j) Is a sub-objective function of the evaluation function MinF, c ₁ 、c ₂ 、c ₃ Respectively is f ₁ (j)、f ₂ (j)、f ₃ (j) M is the number of the first objects, j is more than or equal to 0, m is more than or equal to 0;

f ₁ (j) Denotes the number j ofA workload balance of an object, the workload balance being represented by a variance of an actual operating time period of the first object, w representing the actual operating time period of the first object,

an average value, f, representing the actual operating time of all said first objects ₂ (j) Is the number of the first objects, f ₂ ＝m，f ₃ Representing the working efficiency of the first object, the working efficiency being determined according to the quotient of the task time length of the first object and the actual working time length of the first object, f ₃ (j)＝d _j /w _j And d represents a task time length of the first object.

According to still another embodiment of the present invention, there is provided a processing apparatus of a train task, including: the system comprises a first determining module, a second determining module and a control module, wherein the first determining module is used for dividing each train line according to transfer stations on each train line to obtain a plurality of train tasks, and any one of the plurality of train tasks is used for indicating a running task of a train from one transfer station to another transfer station; a second determining module, configured to determine state information of each train task according to a start-stop station of each train task in the plurality of train tasks, where the state information includes one of: returning to the field, exiting from the field and outputting, and setting; the allocation module is used for allocating a first object to the train task with different state information based on a first algorithm and a second algorithm, wherein the first algorithm at least comprises one of the following steps: a greedy forward algorithm and a greedy reverse algorithm, wherein the second algorithm at least comprises one of the following algorithms: greedy grid algorithm, tabu search algorithm, simulated annealing algorithm.

According to a further aspect of an embodiment of the present invention, there is also provided a computer-readable storage medium having a computer program stored therein, wherein the computer program is configured to perform the method of any one of the above when executed.

According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes any one of the methods described above through the computer program.

According to the invention, through the embodiment, each train line is divided according to the transfer stations on each train line to obtain a plurality of train tasks, and any one of the plurality of train tasks is used for indicating the running task of the train from one transfer station to another transfer station; determining the state information of each train task according to the starting and stopping station of each train task in the plurality of train tasks, wherein the state information comprises one of the following: returning to the field, exiting from the field and outputting, and setting; allocating a first object for the train task with different state information based on a first algorithm and a second algorithm, wherein the first algorithm at least comprises one of the following steps: a greedy forward algorithm and a greedy reverse algorithm, the second algorithm comprising at least one of: a greedy grid algorithm, a tabu search algorithm and a simulated annealing algorithm solve the problem that the distribution result is not ideal (for example, the number of drivers is large) when a single algorithm is used for distributing the first object in the related technology.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:

fig. 1 is a block diagram of a hardware configuration of a computer terminal of a first object allocation method according to an embodiment of the present invention;

FIG. 2 is a flow chart of a method of allocation of a first object according to an embodiment of the invention;

FIG. 3 is a schematic illustration of train mission segmentation according to an embodiment of the present invention;

FIG. 4 is a schematic illustration of status information of a train according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a configuration algorithm assembly flow according to an embodiment of the present invention;

FIG. 6 is a schematic illustration of a positive shift of a train mission according to an embodiment of the present invention;

FIG. 7 is a schematic illustration of a reverse shift of train missions according to an embodiment of the present invention;

FIG. 8 is a schematic diagram of a shift scheduling constraint according to an embodiment of the present invention;

FIG. 9 is a flow chart of a computational principle of a combining algorithm according to an embodiment of the invention;

FIG. 10 is a schematic diagram of an optimal solution list for a combinatorial algorithm according to an embodiment of the invention;

FIG. 11 is a schematic diagram of a greedy inverse ordering algorithm according to an embodiment of the invention;

FIG. 12 is a schematic diagram of a greedy forward algorithm according to an embodiment of the present invention;

FIG. 13 is a schematic diagram of a greedy grid algorithm, according to an embodiment of the present invention;

FIG. 14 is a schematic diagram of a tabu search algorithm according to an embodiment of the present invention;

FIG. 15 is a schematic diagram of a simulated annealing algorithm according to an embodiment of the present invention;

FIG. 16 is a flowchart of a first object allocation method according to an embodiment of the present invention;

FIG. 17 is a flowchart of a method of assigning a first object according to an embodiment of the present invention;

fig. 18 is a block diagram showing a configuration of a train task processing device according to an embodiment of the present invention.

Detailed Description

The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments of the present invention may be combined with each other without conflict.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

The method provided by the embodiment of the invention can be executed in a computer terminal or a similar arithmetic device. Taking the example of the method running on a computer terminal, fig. 1 is a block diagram of a hardware structure of the computer terminal of the method for allocating a first object according to the embodiment of the present invention. As shown in fig. 1, the computer terminal may include one or more processors 102 (only one is shown in fig. 1) (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data, and in an exemplary embodiment, may further include a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the computer terminal. For example, the computer terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration with equivalent functionality to that shown in FIG. 1 or with more functionality than that shown in FIG. 1.

The memory 104 may be used to store computer programs, for example, software programs and modules of application software, such as a computer program corresponding to the allocation method of the first object in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, so as to implement the above-mentioned method. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 can further include memory located remotely from the processor 102, which can be connected to a computer terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.

An embodiment of the present invention provides a method for allocating a first object, which is applied to the computer terminal, and fig. 2 is a flowchart of the method for allocating a first object according to the embodiment of the present invention, and as shown in fig. 2, the flowchart includes the following steps:

step S202, dividing each train line according to the transfer stations on each train line to obtain a plurality of train tasks, wherein any one of the plurality of train tasks is used for indicating a running task of a train from one transfer station to another transfer station;

in one embodiment, the process of obtaining a train mission is described in conjunction with FIG. 3. As shown in fig. 3, the horizontal axis represents the operation time of a subway train (i.e., a train), and the vertical axis represents different trains. The train route of each train at different running times is divided into individual train tasks (i.e. the dashed line in fig. 3) according to the transfer station of the driver, and these train tasks can form a task list of the driver, i.e. a traffic list.

Step S204, determining the state information of each train task according to the starting and stopping station of each train task in the plurality of train tasks, wherein the state information comprises one of the following: returning to the field, exiting from the field and outputting, and aligning;

the starting and stopping stations of each train task comprise a starting station and an ending station of each train task. It should be noted that the starting station is a parking lot or a vehicle section, and the state information of the train task is determined as a departure and departure section; the ending station is a parking lot or a vehicle section, and the state information of the train task is determined to be a return section; and the starting station and the ending station are positive line stations, and the state information of the train task is determined to be a positive line.

In one embodiment, the status information of the train mission may be described with reference to fig. 4, and as shown in fig. 4, when the train travels from the a train segment to the B station, the status information of the train mission is the departure/departure segment. When the train runs from the E station to the parking lot, the state information of the train task is the return section. When the train travels from the C station to the E station on the route 2, the status information of the train task is a positive line.

Step S206, distributing a first object for the train task with different state information based on a first algorithm and a second algorithm, wherein the first algorithm at least comprises one of the following steps: a greedy forward algorithm and a greedy reverse algorithm, the second algorithm comprising at least one of: greedy grid algorithm, tabu search algorithm, simulated annealing algorithm.

According to the embodiment, each train line is divided according to the transfer stations on each train line to obtain a plurality of train tasks, and any one of the train tasks is used for indicating a running task of a train from one transfer station to another transfer station; determining the state information of each train task according to the starting and stopping station of each train task in the plurality of train tasks, wherein the state information comprises one of the following: returning to the field, exiting from the field and outputting, and setting; the first object is distributed to the train tasks with different state information based on the first algorithm and the second algorithm, the first object can be distributed by using multiple algorithms at the same time through algorithm combination of the first algorithm and the second algorithm, and compared with a scheme that the first object can be distributed only by using a single algorithm in the related technology, the scheme solves the problem that the distribution result is not ideal (for example, the number of drivers is large) when the first object is distributed by using the single algorithm in the related technology.

In an optional exemplary embodiment, before allocating the first object to the train task with different state information based on the first algorithm and the second algorithm, the method further comprises the following steps: obtaining scheduling constraint conditions of the plurality of train tasks, wherein the scheduling constraint conditions comprise: a shift dividing parameter, a dining or rest parameter, and a shift workload parameter; generating a scheduling task based on the shift dividing parameter, the dining or rest parameter, and the shift workload parameter, wherein the shift comprises a first shift, a second shift, and a third shift, and the task start time or the task end time of the train task in the first shift, the second shift, and the third shift are different.

It should be noted that the shift dividing parameter is used to divide the shift of train tasks among the shift or multiple train tasks of the driver, the dining or rest parameter is used to arrange the dining or rest time for the first object, and the shift workload parameter is used to constrain the task durations of different drivers.

Wherein the shift dividing parameters include: firstly, selecting whether to divide the shift according to the time of the first task of the driver or the shift used for dividing the operation section according to the time; then, the user selects whether the shift is divided according to the task starting time or the shift is divided according to the ending time when the user returns to the field. If the ' dividing the shift according to the time of the first task of the driver ' is selected ', the shift dividing parameter is used for dividing the shift of the driver; if "the time is used for dividing the shift of the operation section" is selected, the shift division parameter is used for dividing the shift of the train mission.

Wherein the dining or rest parameters include: the Chinese meal dining mode comprises a Chinese meal time range, a Chinese meal time duration, a dinner time range, a dinner place and a dining mode, wherein the dining mode comprises a ready dining mode and a midway dining mode, the ready dining mode indicates that dining is arranged when the dining place is reached in the Chinese meal or dinner time range, and the midway dining mode indicates that dining is arranged when a certain percentage (which can be set) of the workload reaching the upper limit reaches the dining place. The shift workload parameters include: driving time length upper and lower limits, working time length upper and lower limits, driving mileage upper and lower limits and operating section quantity upper and lower limits of each shift.

In an alternative exemplary embodiment, allocating the first object for the train task with different status information based on the first algorithm and the second algorithm comprises: determining a plurality of preset algorithm flows and an algorithm type corresponding to each algorithm flow in the plurality of algorithm flows, wherein the algorithm type at least comprises one of the following steps: the first algorithm, the second algorithm; calculating a plurality of local solutions corresponding to each algorithm flow according to the algorithm type corresponding to each algorithm flow, and combining the local solutions to obtain a target solution corresponding to each algorithm flow, wherein the target solution represents a result of distributing a first object for the train tasks with different state information under each algorithm flow; and determining a preset number of target solutions from the plurality of target solutions corresponding to the plurality of algorithm flows, and distributing a first object for the train tasks with different state information according to the preset number of target solutions.

In one embodiment, a method for processing a train task based on a combined algorithm of the first algorithm and the second algorithm is described in detail, and the specific steps include:

step S10: a combined algorithm flow (equivalent to the above algorithm flow) is configured: the problem that the workload of the returning section (greedy reverse sequence algorithm) and the leaving section (greedy positive sequence algorithm) is not saturated is solved by adopting a determination algorithm, and the problem that the distribution result is not ideal (for example, the number of drivers is large) when a single algorithm is used for distributing the first object is solved by adopting a high-level algorithm to calculate the residual tasks to realize a combined algorithm.

As shown in fig. 5, various types of algorithms can be accessed in the algorithm flow architecture to implement algorithm combination flexible configuration. For example, in one embodiment, in the algorithm flow, a greedy reverse order algorithm is used to assign drivers to first train task state information of a first object (i.e., driver) in a first shift for train tasks in a return to field section, then a greedy forward order algorithm is used to assign drivers to unassigned train tasks in the first shift, a greedy forward order algorithm is used to assign drivers to first train task state information of drivers for train tasks in a departure to field section in a second shift and a third shift, a greedy reverse order algorithm is used to assign drivers to first train task state information of drivers for train tasks in a return to field section in a second shift and a third shift, and a greedy mesh algorithm is used to assign drivers to unassigned train tasks in a second shift and a third shift.

In one embodiment, the parameters of each flow node in the combined algorithm configuration flow include: the number of shifts, the type of the train number, the type of the algorithm, the sequence of the algorithm, the upper limit of the number of people and the calculation time. The configuring of the site parameters of the process nodes includes: the lower limit and the upper limit of the rest time of the transfer room of each station, whether the return section continues to connect the task, the reserved time of the connected task and the target station of the connected task.

In one embodiment, a result of assigning drivers using a greedy positive sequence algorithm is illustrated in connection with FIG. 6. As shown in fig. 6, assuming that the target task amount allocated to the driver is 3, and the task state information of the task "7" is the return-to-field return segment, the task "1", the task "3", and the task "5" are allocated to the driver 1 in the forward order, the task "2", the task "4", and the task "6" are allocated to the driver 2, and at this time, the driver 3 is allocated to the task "7", and the driver 3 executes the return-to-field return segment of the task "7", and then the subsequent tasks are not performed, and the task amount of the driver 3 in the allocation result is not saturated.

In one embodiment, the result of assigning drivers using a greedy reverse order algorithm in combination with a greedy forward order algorithm is illustrated in connection with FIG. 7. The principle of the greedy reverse shift algorithm is explained in conjunction with fig. 7. As shown in fig. 7, assuming that the target task amount allocated to the driver is 3, the task state information of the task "7" is the return section, and the shift is performed by using the greedy reverse-order algorithm, the task "7", the task "5", and the task "3" are allocated to the driver 1 in the reverse order, the task "1", the task "4", and the task "8" are allocated to the driver 2 in the forward order, and at this time, the task "2", the task "6", and the task "10" are allocated to the driver 3 in the forward order. The task load of all drivers in the allocation results is saturated.

Further, in the above embodiment, the use of a separate greedy positive sequence algorithm for allocating drivers is prone to the problem of non-saturation of the workload of the roundtrip segment, for example, the driver 3 in fig. 6 is not saturated in workload. The problem that the workload of the return section is not saturated can be solved by using a greedy reverse order algorithm, for example, in fig. 7, the workload distributed to the driver 1 is saturated.

Step S20: and (3) configuring algorithm parameters: and configuring parameters such as shift, dining, rest and the like as shift scheduling constraint conditions. As shown in fig. 8, the shift scheduling constraints include: a shift division parameter, a dining or rest parameter, a shift workload parameter.

Wherein, the dividing parameter of the shift includes: whether the time of a first task of a driver is used for dividing the shift or the shift of a time for dividing an operation section is selected, whether the second-shift task starting time of each station is the first-shift ending time of the shift and the field section station is divided according to the task ending time when the station returns to the field and returns to the section is selected.

The dining or rest parameters include: chinese meal time range, time length, dinner time range, time length, dining place and dining mode.

The work shift parameters include: the driving time length upper and lower limits, the working time length upper and lower limits, the driving mileage upper and lower limits and the number upper and lower limits of the operation sections of each shift.

Step S30: and calculating the distribution result according to the configured combination algorithm. As shown in fig. 9, the calculation flow is as follows:

step S901, determining K combined algorithm flows configured in advance. Starting from the 1 st algorithm flow, one by one, j =1 is calculated.

And step S902, calculating a corresponding local solution by using the algorithm type of the jth algorithm process.

In step S903, the local solution is returned.

In one embodiment, where the local solution for each algorithm includes a plurality, a certain number of local solutions may be obtained. For example, the uncertainty algorithm returns 10 optimal solutions according to the objective function.

In step S904, the local solutions are merged into a target solution (corresponding to the above-described allocation result of the first object to the train task of different status information).

Step S905, executing a j + + program;

step S906, determining whether j is less than or equal to K, if yes, executing step S902, otherwise, executing step S907.

In step S907, a certain number (corresponding to the preset number) of target solutions are obtained. For example, the uncertainty algorithm returns 10 optimal solutions according to the objective function.

Through the steps, calculation is sequentially carried out according to the algorithm flow, local solutions are generated, the local solutions are combined to obtain a target solution, the optimal 10 solutions are returned according to the target function, and a small number of people can be calculated in a short calculation time. As shown in table 1, compared with performing calculation by using a single algorithm in sequence, the combined algorithm flow has a smaller number of people and a medium calculation time, and the calculation time is longer than that of a greedy forward-sequence algorithm and a greedy reverse-sequence algorithm and shorter than that of a greedy grid algorithm.

TABLE 1

For example, the calculation time obtained by the greedy forward sequence algorithm, the greedy reverse sequence algorithm, and the greedy trellis algorithm is 2 seconds, and 2 minutes, respectively. Specifically, only greedy positive sequence calculation is adopted, the calculation time is 2 seconds, and the number of people is 96; only greedy reverse order calculation is used, the calculation time is 2 seconds, and the number of people is 101; only with greedy grid calculation, the calculation time was 2 points, and the number of people was 93. The typical flow calculation of the combined algorithm is adopted, the calculation time is 1 minute and 10 seconds, and the number of people is 91.

Step S40: and selecting the optimal solution from the obtained optimal solution list. As shown in fig. 10, the optimal solution list may display the returned solutions, which may be browsed and selected. The optimal solution list shows 10 returned solutions, and taking the first solution as an example, the objective function value can be obtained as 114009560, the number of drivers is 114, the number of drivers assigned to the early shift (equivalent to the first shift) is 37, the number of drivers assigned to the white shift (equivalent to the second shift) is 40, the number of drivers assigned to the late shift (equivalent to the third shift) is 37, the balance degree corresponding to the first solution is 95.60, and the racing rate is 71.05%.

According to the technical scheme of the embodiment, the algorithm process is realized by combining single algorithms, the advanced algorithm search space is reduced, the calculation time is greatly shortened, and the corresponding algorithm types are locally adopted to solve different problems, so that the workload of driver distribution is balanced, the running rate is high, the number of drivers is correspondingly small, the distribution efficiency of train tasks is improved, the distribution quality of the train tasks is improved, and the problems of missing arrangement, wrong arrangement and long calculation time are solved.

And the algorithm flow can be customized according to the characteristics of various running routes only by adjusting the combination sequence of the algorithm flow, the flexibility is high, the strain capacity is strong, and the algorithm flow is reset if the running route is adjusted.

In an alternative exemplary embodiment, in order to better understand how to allocate the first object to the train tasks with different status information based on the first algorithm and the second algorithm in step S206, the status information of the first train task of the first object in the first shift may be calculated in a reverse order according to the greedy reverse order algorithm to be a first allocation result of the train task of the return field and the return section; calculating a second distribution result of the unallocated train tasks in the first shift according to the positive sequence of the greedy positive sequence algorithm; according to the greedy positive sequence algorithm, calculating the state information of the first train task of the first object in the second shift and the third shift in a positive sequence manner to be a third distribution result of the train tasks of the departure and departure sections; calculating the state information of the first train task of the first object in the second shift and the third shift according to the greedy reverse order algorithm in a reverse order mode, wherein the state information is the fourth distribution result of the train tasks of the return section; calculating a fifth distribution result of the unallocated train tasks in the second shift and the third shift according to the second algorithm; and determining the sum of the first distribution result, the second distribution result, the third distribution result, the fourth distribution result and the fifth distribution result as a distribution result of distributing a first object to the train tasks with different state information.

In an optional exemplary embodiment, the specifically calculating, according to the greedy reverse ordering algorithm, that the state information of the first train task of the first object in the first shift is the first distribution result of the train tasks of the return segment includes: firstly, sequencing all train tasks in a reverse order according to task ending time to obtain a first train task list after sequencing in the reverse order; and distributing the first object to the train task belonging to the first shift in the first train task list according to the greedy reverse order algorithm, wherein the state information of the first train task of the first object is the train task of the return section to obtain a first distribution result, and the first distribution result comprises a first task amount distributed to the first object.

In an embodiment, a process of allocating the first object to the train task is described with reference to the greedy reverse order algorithm principle in fig. 11, which is specifically as follows:

and S1101, sequencing all the N train tasks in a reverse order according to the task ending time to obtain a result after the reverse order is sequenced, and starting to solve circularly from the 1 st task in the result after the reverse order is sequenced, namely i =1, and allocating drivers one by one, wherein N is a positive integer greater than or equal to 1, and i is greater than or equal to 1 and is less than or equal to N.

Step S1102, searching drivers waiting at the finishing station of the ith train task;

step S1103, determining whether a driver waiting for allocation at the end station of the ith train mission is found, if yes, executing step S1104, and if no, executing step S1107;

step S1104, determining one or more drivers waiting to be allocated at the end station, and allocating the ith train task to the driver with the longest waiting time;

step S1105, executing the program of i + +;

step S1106, judging whether the N is equal to or less than the N, if so, executing step S1102, otherwise, executing step S1109;

step S1107, judging whether the status information of the ith train task is the return-to-field section and whether the shift is the first shift, if so, executing step S1108, and if not, executing step S1105;

step S1108, determining that 1 new driver is scheduled to the end station, allocating the ith train task to the 1 new driver, and executing step S1105;

and S1109, confirming that the train tasks after the reverse sequencing are completely distributed, and obtaining a result m1 distributed to drivers, namely the train tasks distributed to each driver.

Through the steps, the state information of the first train task of the driver in the first shift is distributed to the train tasks of the return section of the station, the distribution result of the driver is obtained, and the number of the drivers distributed to the train tasks and the task quantity of the drivers are determined.

In an optional exemplary embodiment, the step of forward-sequence calculating the second distribution result of the train tasks not distributed in the first shift according to the greedy forward-sequence algorithm includes: firstly, performing positive sequence sequencing on the unallocated train tasks according to task starting time to obtain a positive sequence sequenced second train task list; and distributing the first object to the train tasks belonging to the first shift in the second train task list according to the greedy positive sequence algorithm to obtain a second distribution result, wherein the second distribution result comprises a second task amount distributed to the first object.

In an embodiment, a process of allocating the first object to the train task is described with reference to the greedy positive sequence algorithm principle in fig. 12, which is specifically as follows:

step S1201, performing positive sequence sequencing on N2 unallocated train tasks according to task starting time to obtain a positive sequence sequenced result, starting to solve circularly from the 1 st task in the positive sequence sequenced result, namely i =1, and allocating drivers one by one, wherein N2 is a positive integer larger than or equal to 1, and i is larger than or equal to 1 and is smaller than or equal to N2.

Step S1202, searching drivers waiting for distribution at the starting station of the ith train task;

step S1203, determining whether a driver waiting for distribution at a starting station of an ith train task is found, if so, executing step S1204, and if not, executing step S1207;

step S1204, confirm waiting for one or more drivers distributed at the starting station, distribute the ith train task to waiting for the driver of the longest time;

step S1205, the program of i + + is executed;

step S1206, judging whether the N2 is equal to or less than i, if so, executing step S1202, otherwise, executing step S1209;

step S1207, judging whether the shift of the ith train task is the first shift, if so, executing step S1208, and if not, executing step S1205;

step S1208, determining that 1 new driver is dispatched to the starting station, allocating the ith train task to the 1 new driver, and executing step S1205;

and step S1209, the train tasks after the positive sequence sequencing are confirmed to be completely distributed, and a result m2 distributed to drivers is obtained, namely the train task distributed to each driver.

By the steps and the greedy positive sequence algorithm, the train tasks which are not distributed in the first shift are distributed, the driver distribution result is obtained, and the number of drivers distributed to the train tasks and the driver task amount are determined.

In an alternative exemplary embodiment, a fifth assignment result of the unassigned train tasks in the second shift and the third shift according to the second algorithm is provided, specifically: firstly, carrying out positive sequence sequencing on unallocated train tasks according to task starting time to obtain a fifth train task list subjected to positive sequence sequencing; and distributing the first object to the trains belonging to the second shift and the third shift in the fifth train task list according to the second algorithm to obtain a fifth distribution result, wherein the fifth distribution result comprises a fifth task amount distributed to the first object.

Optionally, in an embodiment, a process of allocating the first object to the train task is described with reference to the greedy grid algorithm principle in fig. 13, which is specifically as follows:

step S1301, calculating the number of drivers to be M1 based on a greedy positive sequence algorithm principle for unallocated train tasks;

step S1302, supposing that x drivers are sequentially distributed to the train task in a positive sequence, and circularly solving from x =0, wherein x is more than or equal to 0 and is less than or equal to M1;

step S1303, distributing tasks to x drivers based on a greedy positive sequence algorithm principle;

step S1304, calculating other unallocated tasks by using a greedy reverse order algorithm principle;

step S1305, obtaining a solution;

step 1306, executing the program of x + +;

step 1307, determining whether x is greater than or equal to M1, if yes, executing step 1303 to step 1306 in a loop, and if no, executing step 1308;

step S1308, M1+1 solutions are obtained.

Step 1309, calculating the number of drivers as M2 based on the greedy reverse order algorithm principle;

step 1310, supposing that y drivers are sequentially distributed for the reverse sequence of the train task, and circularly solving from y =0, wherein y is more than or equal to 0 and is less than or equal to M2;

step S1311, calculating y drivers based on a greedy reverse order algorithm principle, and distributing tasks for the y drivers;

step S1312, calculating other unallocated tasks by using a greedy positive sequence algorithm principle;

step S1313, obtaining a solution;

step S1314, execute the program of y + +;

step S1315, judging whether y is less than or equal to M2, if yes, executing step S1311 to step S1314 in a circulating mode, and if not, executing step S1316;

and step S1316, obtaining M2+1 species.

Step S1317, determining that M1+ M2+2 solutions are obtained based on the above steps.

In step S1318, the optimal solution of the M1+ M2+2 solutions is determined based on the objective function (corresponding to the above evaluation function).

Based on the greedy grid algorithm principle, rapid calculation can be achieved, for example, the calculation time of a primary forward sequence or a primary reverse sequence is approximately equal to 2 seconds, and the method has a unique solution, namely the calculation results of each time are the same, accords with the thought of scheduling for a driver, and has high racing rate. The locally optimal solution may be determined using the greedy trellis algorithm described above.

Optionally, in an embodiment, a process of allocating the first object to the train task is described with reference to a principle of a tabu search algorithm in fig. 14, specifically as follows:

step S1401, calculating the unallocated train task by using a greedy positive sequence algorithm, and taking an obtained solution as an initial solution of a tabu search algorithm;

step S1402, first randomly selecting a driver 1 with unqualified task volume and unlocked task, and then randomly selecting an unlocked task a from the task set of the driver 1, wherein the unqualified task volume means that the actually distributed task volume is smaller than the preset distributed task volume.

Step S1403, a neighborhood range of task a is generated.

Task a may be a train task from site 1 to site 2 between 9 and 30 minutes, and the neighborhood range of task a includes tasks transmitted from site 2 to other sites within 20 minutes after 9 and 30 minutes.

Step S1404, after the task a and other tasks in the neighborhood range of the task a are completely allocated, obtaining an optimal solution from the allocation results of the task a and other tasks in the neighborhood range of the task a by using the objective function. The driver of task a and the task of the driver are locked.

Step S1405, determining whether the tasks are all in the locked state, if yes, performing step S1406, and if no, performing step S1402.

In step S1406, an optimal solution is obtained.

Alternatively, in one embodiment, the computational principle of the simulated annealing algorithm may be represented by the following formula:

ΔT＝F(S′)-F(S)(1)，

wherein T represents the initial temperature, S represents the initial solution state, and a new solution S' is obtained after L iterations of each T value. And if the delta T is less than 0, accepting S 'as a new current solution, otherwise, accepting S' as a new current solution by using the probability P. Wherein P = exp (- Δ T/T).

In an embodiment, a process of allocating the first object to the train task is described with reference to the simulated annealing algorithm principle in fig. 15, which is as follows:

step S1501, calculating the unallocated train tasks by using a greedy positive sequence algorithm, and calculating a target function by using an obtained solution as an initial solution of a simulated annealing algorithm;

step S1502, randomly acquiring two intersection lists, and respectively taking out a task B and a task C from the two intersection lists;

wherein the intersection table represents a task set of the first object.

In step S1503, it is checked whether task B and task C can be exchanged according to the constraint condition, if yes, step S1504 is executed, and if no, step S1512 is executed.

And if the starting sites of the task B and the task C are the same and belong to the inter-break time range, exchanging the task B and the task C.

Step S1504 exchanges task B and task C.

In step S1505, it is determined whether the two traffic lists can be merged, wherein the number of drivers can be reduced after merging the traffic lists. If yes, step S1506 is executed, otherwise, step S1512 is executed.

And step S1506, combining the two intersection tables and calculating the objective function value.

In step S1507, it is determined whether the objective function value becomes small, and if so, step S1508 is performed, otherwise, step S1511 is performed.

Step 1508, accepting the annealing operation.

Step S1509, it is determined whether the number of calculation times reaches a preset number of algorithm iterations, if so, step S1510 is executed, otherwise, step S1502 is executed.

Step S1510 determines whether a loop termination condition is satisfied, and if so, step S1513 is executed, that is, the objective function value calculated in step S1506 is used as the optimal solution. Otherwise, step S1502 is executed.

Step S1511, the annealing operation is performed with a certain probability.

In step S1512, the annealing operation is rejected.

And step S1513, obtaining the optimal solution.

In an alternative exemplary embodiment, after allocating the first object to the train task with different status information based on the first algorithm and the second algorithm, the allocation result of the first object may be further evaluated by using an evaluation function MinF, so as to obtain an evaluation function value, where the evaluation function MinF is as follows:

wherein, f ₁ (j)、f ₂ (j)、f ₃ (j) As a sub-objective function of the evaluation function MinF, c ₁ 、c ₂ 、c ₃ Respectively is f ₁ (j)、f ₂ (j)、f ₃ (j) M is the number of the first objects, j is more than or equal to 0, m is more than or equal to 0;

f ₁ (j) Representing a workload balance of a first object of sequence number j, the workload balance being represented by a variance of an actual operating time length of the first object, w representing the actual operating time length of the first object,

representing the average of the actual operating times of all said first objects, f ₂ (j) Is the number of the first objects, f ₂ ＝m，f ₃ Representing the working efficiency of the first object, the working efficiency being determined according to the quotient of the task time length of the first object and the actual working time length of the first object, f ₃ (j)＝d _j /w _j And d represents a task time length of the first object.

In an optional exemplary embodiment, the specifically calculating, according to the greedy positive sequence algorithm, the state information of the first train task of the first object in the second and third shifts in positive sequence is a third allocation result of the train task that is out of field and out of range includes: firstly, carrying out positive sequence sequencing on unallocated train tasks according to task starting time to obtain a third train task list after positive sequence sequencing; and distributing the first object to the train task which belongs to the second shift and the third shift in the third train task list according to the greedy positive sequence algorithm, wherein the state information of the first train task of the first object is the train task which is out of the field and out of the section, and obtaining a third distribution result, wherein the third distribution result comprises a third task amount distributed to the first object.

In an embodiment, a process of allocating the first object to the train task is described with reference to fig. 16, specifically as follows:

and S1601, performing positive sequence sequencing on the N3 unallocated train tasks according to task starting time to obtain a result after the positive sequence sequencing, and starting to solve circularly from the 1 st task in the result after the positive sequence sequencing, namely i =1, and allocating drivers one by one, wherein N3 is a positive integer larger than or equal to 1, and i is larger than or equal to 1 and is smaller than or equal to N3.

Step S1602, searching drivers waiting at the starting station of the ith train task;

step S1603, determining whether a driver waiting for distribution at the starting station of the ith train task is found, if so, executing step S1604, and if not, executing step S1607;

step S1604, determining one or more drivers waiting to be allocated at the starting station, and allocating the ith train task to the driver with the longest waiting time;

step S1605, executing the program of i + +;

step S1606, judging whether the N3 is equal to or less than i, if so, executing step S1602, otherwise, executing step S1609;

step S1607, judging whether the status information of the ith train task is the departure and departure section and whether the shift is the second shift or the third shift, if yes, executing step S1608, if no, executing step S1605;

step S1608, determining that 1 new driver is scheduled to the start station, allocating the ith train mission to the 1 new driver, and executing step S1605;

step S1609, the train tasks after the positive sequence sorting are confirmed to be distributed, and a result m3 distributed to drivers is obtained, namely the train tasks distributed to each driver.

In an optional exemplary embodiment, the step of calculating the state information of the first train task of the first object in the second shift and the third shift in reverse order according to the greedy reverse order algorithm is a fourth distribution result of the train tasks in the field return and section return section comprises the following specific steps of: firstly, performing reverse sequencing on unallocated train tasks according to task ending time to obtain a fourth train task list subjected to reverse sequencing; and distributing the first object to the train task of the return section of the train task in the fourth train task list according to the greedy reverse order algorithm to obtain a fourth distribution result, wherein the fourth distribution result comprises a fourth task amount distributed to the first object.

In an embodiment, a process of allocating the first object to the train task is described with reference to fig. 17, which includes the following steps:

and step 1701, sequencing N4 train tasks which are not distributed in a reverse order according to task ending time to obtain a result after the reverse order is sequenced, starting to solve circularly from the 1 st task in the result after the reverse order, namely i =1, and distributing drivers one by one, wherein N4 is a positive integer which is more than or equal to 1, and i is more than or equal to 1 and is less than or equal to N4.

Step 1702, searching drivers waiting for distribution at the end station of the ith train task;

step S1703, determining whether a driver waiting for allocation at the finishing station of the ith train task is found, if so, executing step S1704, and if not, executing step S1707;

step S1704, determining one or more drivers waiting to be distributed at the end station, and distributing the ith train task to the driver with the longest waiting time;

step S1705, executing the program of i + +;

step S1706, judging whether i is less than or equal to N4, if so, executing step S1702, otherwise, executing step S1709;

step S1707, judging whether the status information of the ith train task is the return section, and the shift is the second shift or the third shift, if so, executing step S1708, otherwise, executing step S1705;

step S1708, determining that 1 new driver is dispatched to the end station, distributing the ith train task to the 1 new driver, and executing step S1705;

and step S1709, the train tasks after the reverse sequencing are confirmed to be completely distributed, and a result m4 distributed to drivers is obtained, namely the train tasks distributed to each driver.

In one embodiment, different types of algorithms are compared, as shown in table 2, and the different algorithms are compared from algorithm type, algorithm order, calculation time, and calculation result, respectively.

TABLE 2

For example, the greedy grid algorithm has a fast calculation speed, takes about 2 minutes to complete one calculation, can obtain a local optimal solution, and has a high running rate and a large number of required drivers.

The taboo search algorithm is low in calculation speed, about 20 minutes are needed for completing one calculation, a calculation result close to the global optimal solution can be obtained, the running rate is low, and the number of required drivers is small.

The simulated annealing algorithm is slow in calculation speed, about 30 minutes is needed for completing one calculation, a calculation result close to the global optimal solution can be obtained, the running rate is low, and the number of drivers is small.

By the embodiment, through analyzing the scheduling characteristics, combining the steps and experience of manual scheduling, aiming at the defects of potential safety hazards, long time consumption, low quality and poor strain capacity of manual scheduling, and the defects that an optimization method needs huge time overhead and a heuristic method is very easy to be not converged or premature, an algorithm combination flow concept is introduced, corresponding algorithm types are locally adopted to solve different problems, and then the drivers are distributed according to a flow sequence combination scheme, so that the greedy forward algorithm positive sequence distribution driver, the greedy reverse algorithm reverse sequence distribution driver, the greedy grid algorithm distribution driver, a taboo search algorithm distribution driver and a simulated annealing algorithm distribution driver are realized, and algorithm combination flexible activation configuration is realized on the basis of algorithm flow architectures which can be accessed to various algorithms, so that the optimal solution can be directly determined according to a target function after algorithm combination.

Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.

According to another embodiment of the present invention, a processing device for train tasks is provided, which is used for implementing the foregoing embodiment and preferred embodiments, and which has been described above and will not be described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function.

An embodiment of the present invention further provides a processing device for a train task, as shown in fig. 18, fig. 18 is a block diagram of a structure of the processing device for a train task according to an embodiment of the present invention, including:

the first determining module 1802 is configured to divide each train route according to a transfer station on each train route to obtain a plurality of train tasks, where any train task of the plurality of train tasks is used to indicate a running task of a train from one transfer station to another transfer station;

a second determining module 1804, configured to determine status information of each train task according to a start-stop station of each train task in the plurality of train tasks, where the status information includes one of: returning to the field, exiting from the field and outputting, and setting;

the starting and stopping stations of each train task comprise a starting station and an ending station of each train task. It should be noted that the starting station is a parking lot or a vehicle section, and the state information of the train task is determined as a departure section; the ending station is a parking lot or a vehicle section, and the state information of the train task is determined to be a return section; and the starting station and the ending station are positive line stations, and the state information of the train task is determined to be a positive line.

An allocating module 1806, configured to allocate a first object to a train task with different status information based on a first algorithm and a second algorithm, where the first algorithm at least includes one of: a greedy forward algorithm and a greedy reverse algorithm, wherein the second algorithm at least comprises one of the following algorithms: greedy grid algorithm, tabu search algorithm, simulated annealing algorithm.

According to the device, each train line is divided according to the transfer stations on each train line to obtain a plurality of train tasks, any one of the train tasks is used for indicating a running task of a train from one transfer station to another transfer station, and the state information of each train task is determined according to the starting and stopping stations of each train task in the plurality of train tasks, wherein the state information comprises one of the following information: returning to the field, exiting from the field and outputting, and setting; the first object is distributed to the train tasks with different state information based on the first algorithm and the second algorithm, and the problem that the distribution result is not ideal (for example, the number of drivers is large) when the first object is distributed by using a single algorithm in the related art is solved.

In an optional exemplary embodiment, the allocating module 1806 is further configured to determine a plurality of preconfigured algorithm flows and an algorithm type corresponding to each algorithm flow in the plurality of algorithm flows, where the algorithm type includes at least one of: the first algorithm, the second algorithm; calculating a plurality of local solutions corresponding to each algorithm flow according to the algorithm type corresponding to each algorithm flow, and combining the local solutions to obtain a target solution corresponding to each algorithm flow, wherein the target solution represents a result of distributing a first object for the train tasks with different state information under each algorithm flow; and determining a preset number of target solutions from the plurality of target solutions corresponding to the plurality of algorithm flows, and distributing a first object for the train tasks with different state information according to the preset number of target solutions.

In an optional exemplary embodiment, the processing device for train task further includes: an obtaining module, configured to obtain scheduling constraints of the train tasks, where the scheduling constraints include: a shift dividing parameter, a dining or rest parameter, and a shift workload parameter; generating a scheduling task based on the shift dividing parameter, the dining or rest parameter, and the shift workload parameter, wherein the shift comprises a first shift, a second shift, and a third shift, and the task start time or the task end time of the train task in the first shift, the second shift, and the third shift are different.

It should be noted that the shift dividing parameter is used to divide shift of train tasks in a plurality of train tasks, the dining or rest parameter is used to arrange dining or rest time for the first object, and the shift workload parameter is used to constrain task durations of different shifts.

Wherein the shift dividing parameters comprise: firstly, selecting whether the shift is divided according to the time of the first task of the driver or the shift of the operation section is divided according to the time; then, the user selects whether the shift is divided according to the task starting time or the shift is divided according to the ending time when the user returns to the field. If the ' dividing the shift according to the time of the first task of the driver ' is selected ', the shift dividing parameter is used for dividing the shift of the driver; if the 'time is used for dividing the shift of the operation section' is selected, the shift dividing parameter is used for dividing the shift of the train task.

In an optional exemplary embodiment, the allocating module 1806 is further configured to calculate, according to the greedy reverse order algorithm, that the state information of the first train task of the first object in the first shift is the first allocation result of the train task of the return segment; calculating a second distribution result of the train tasks which are not distributed in the first shift in a positive sequence mode according to the greedy positive sequence algorithm; calculating the state information of the first train task of the first object in the second shift and the third shift according to the greedy positive sequence algorithm, wherein the state information is a third distribution result of the train tasks which are out of the field and out of the section; calculating the state information of the first train task of the first object in the second shift and the third shift according to the greedy reverse order algorithm in a reverse order mode, wherein the state information is the fourth distribution result of the train tasks of the return section; calculating a fifth distribution result of the unallocated train tasks in the second shift and the third shift according to the second algorithm; and determining the sum of the first distribution result, the second distribution result, the third distribution result, the fourth distribution result and the fifth distribution result as a distribution result of distributing a first object to the train tasks with different state information.

In an optional exemplary embodiment, the allocating module 1806 is further configured to perform reverse ordering on all the train tasks according to the task ending time, so as to obtain a first train task list after the reverse ordering; and distributing the first object for the train task belonging to the first shift in the first train task list according to the greedy reverse order algorithm, wherein the state information of the first train task of the first object is the train task in the return section of the station and the return section, and obtaining a first distribution result, wherein the first distribution result comprises a first task amount distributed for the first object.

In an optional exemplary embodiment, the allocating module 1806 is further configured to perform forward sequencing on the unallocated train tasks according to the task start time, so as to obtain a second train task list after the forward sequencing; and distributing the first object to the trains belonging to the first shift in the second train task list according to the greedy positive sequence algorithm to obtain a second distribution result, wherein the second distribution result comprises a second task amount distributed to the first object.

In an optional exemplary embodiment, the allocating module 1806 is further configured to perform forward sequencing on all train tasks according to the task start time, so as to obtain a forward sequenced third train task list; and distributing the first object to the train task which is out of the field and out of the section according to the state information of the first train task of the first object in the third train task list, and obtaining a third distribution result, wherein the third distribution result comprises a third task amount distributed to the first object.

In an optional exemplary embodiment, the allocating module 1806 is further configured to sort all train tasks in a reverse order according to the task ending time, and obtain a fourth train task list sorted in the reverse order; and distributing the first object to the train of the return section of the train in the fourth train task list according to the greedy reverse order algorithm to obtain a fourth distribution result, wherein the fourth distribution result comprises a fourth task amount distributed to the first object.

In an optional exemplary embodiment, the allocating module 1806 is further configured to perform forward sequencing on the train tasks that are not allocated according to the task start time, so as to obtain a fifth train task list after the forward sequencing; and allocating the first object to the trains belonging to the second shift and the third shift in the fifth train task list according to the second algorithm to obtain a fifth allocation result, wherein the fifth allocation result comprises a fifth task amount allocated to the first object.

In an optional exemplary embodiment, the processing device for train task further includes: an evaluation module, configured to evaluate the allocation result of the first object by using an evaluation function MinF to obtain an evaluation function value, where the evaluation function MinF is as follows:

f ₁ (j) Indicating the workload balance of the first object with the sequence number j, the workload balance being indicated by the variance of the actual operating time length of the first object, w indicating the actual operating time length of the first object,

an average value, f, representing the actual operating time of all said first objects ₂ (j) Is the number of the first objects, f ₂ ＝m，f ₃ Representing the working efficiency of the first object, the working efficiency being determined according to the quotient of the task duration of the first object and the actual working duration of the first object, f ₃ (j)＝d _j /w _j And d represents a task time length of the first object.

An embodiment of the present invention further provides a storage medium including a stored program, wherein the program executes any one of the methods described above.

Alternatively, in this embodiment, the storage medium may be configured to store program codes for performing the following steps:

the method comprises the following steps that S1, each train line is divided according to transfer stations on each train line to obtain a plurality of train tasks, and any one train task in the plurality of train tasks is used for indicating a running task of a train from one transfer station to another transfer station;

s2, determining the state information of each train task according to the starting and stopping station of each train task in the plurality of train tasks, wherein the state information comprises one of the following information: returning to the field, exiting from the field and outputting, and setting;

s3, distributing a first object for the train tasks with different state information based on a first algorithm and a second algorithm, wherein the first algorithm at least comprises one of the following steps: a greedy forward algorithm and a greedy reverse algorithm, the second algorithm comprising at least one of: greedy grid algorithm, tabu search algorithm, simulated annealing algorithm.

Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.

Optionally, for a specific example in this embodiment, reference may be made to the examples described in the above embodiment and optional implementation, and this embodiment is not described herein again.

Embodiments of the present invention further provide an electronic device, comprising a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.

Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.

Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:

the method comprises the following steps that S1, each train line is divided according to transfer stations on each train line to obtain a plurality of train tasks, and any one of the plurality of train tasks is used for indicating a running task of a train from one transfer station to another transfer station;

s2, determining the state information of each train task according to the starting and stopping station of each train task in the plurality of train tasks, wherein the state information comprises one of the following information: returning to the field, exiting from the field and outputting, and aligning;

s3, distributing a first object for the train tasks with different state information based on a first algorithm and a second algorithm, wherein the first algorithm at least comprises one of the following steps: a greedy forward algorithm and a greedy reverse algorithm, wherein the second algorithm at least comprises one of the following algorithms: greedy grid algorithm, tabu search algorithm and simulated annealing algorithm.

Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again.

It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for assigning a first object, comprising:

dividing each train line according to the transfer stations on each train line to obtain a plurality of train tasks, wherein any one train task in the plurality of train tasks is used for indicating a running task of a train from one transfer station to another transfer station;

determining the state information of each train task according to the starting and stopping station of each train task in the plurality of train tasks, wherein the state information comprises one of the following: returning to the field, exiting from the field and outputting, and aligning;

allocating a first object for the train task with different state information based on a first algorithm and a second algorithm, wherein the first algorithm at least comprises one of the following steps: a greedy forward algorithm and a greedy reverse algorithm, wherein the second algorithm at least comprises one of the following algorithms: greedy grid algorithm, tabu search algorithm and simulated annealing algorithm.

2. The first object distribution method according to claim 1, wherein distributing the first object to the train task with different status information based on the first algorithm and the second algorithm comprises:

determining a plurality of preset algorithm flows and an algorithm type corresponding to each algorithm flow in the plurality of algorithm flows, wherein the algorithm type at least comprises one of the following steps: the first algorithm, the second algorithm;

calculating a plurality of local solutions corresponding to each algorithm flow according to the algorithm type corresponding to each algorithm flow, and combining the local solutions to obtain a target solution corresponding to each algorithm flow, wherein the target solution represents a result of distributing a first object for the train tasks with different state information under each algorithm flow;

and determining a preset number of target solutions from the target solutions corresponding to the algorithm flows, and distributing a first object for the train tasks with different state information according to the preset number of target solutions.

3. The method of assigning a first object according to claim 1, wherein before assigning the first object to the train mission having different status information based on the first algorithm and the second algorithm, the method further comprises:

obtaining scheduling constraint conditions of the plurality of train tasks, wherein the scheduling constraint conditions comprise: a shift dividing parameter, a dining or rest parameter, and a shift workload parameter;

generating a scheduling task based on the shift dividing parameter, the dining or resting parameter, and the shift workload parameter, wherein the shift comprises a first shift, a second shift, and a third shift, and the task start time or task end time of the train tasks in the first shift, the second shift, and the third shift are different.

4. The first object distribution method according to claim 3, wherein distributing the first object to the train task of different status information based on the first algorithm and the second algorithm comprises:

according to the greedy reverse order algorithm, calculating the state information of the first train task of the first object in the first shift in a reverse order mode, wherein the state information is a first distribution result of the train tasks of the return section;

calculating a second distribution result of the unallocated train tasks in the first shift according to the positive sequence of the greedy positive sequence algorithm;

calculating the state information of the first train task of the first object in the second shift and the third shift according to the greedy positive sequence algorithm, wherein the state information is a third distribution result of the train tasks which are out of the field and out of the section;

calculating the state information of the first train task of the first object in the second shift and the third shift according to the greedy reverse order algorithm in a reverse order mode, wherein the state information is a fourth distribution result of the train tasks of the return section;

calculating fifth distribution results of the unallocated train tasks in the second shift and the third shift according to the second algorithm;

and determining the sum of the first distribution result, the second distribution result, the third distribution result, the fourth distribution result and the fifth distribution result as a distribution result of distributing a first object to the train tasks with different state information.

5. The method for allocating the first object according to claim 4, wherein calculating the state information of the first train task of the first object in the first shift according to the inverse greedy inverse sequence algorithm in the inverse sequence manner as the first allocation result of the train task of the return section comprises:

sequencing all the train tasks in a reverse order according to the task ending time to obtain a first train task list after sequencing in the reverse order;

and distributing the first object for the train task belonging to the first shift in the first train task list according to the greedy reverse order algorithm, wherein the state information of the first train task of the first object is the train task in the return section of the station and the return section, and obtaining a first distribution result, wherein the first distribution result comprises a first task amount distributed for the first object.

6. The method of assigning a first object according to claim 4, wherein calculating a second assignment of the unassigned train task in the first shift according to the greedy positive sequence algorithm positive sequence comprises:

performing positive sequence sequencing on the unallocated train tasks according to the task starting time to obtain a positive sequence sequenced second train task list;

and distributing the first object to the trains belonging to the first shift in the second train task list according to the greedy positive sequence algorithm to obtain a second distribution result, wherein the second distribution result comprises a second task amount distributed to the first object.

7. The method of claim 4, wherein calculating the state information of the first train task of the first object in the second shift and the third shift according to the greedy positive sequence algorithm positive sequence to be the third distribution result of the train task of the departure and departure segment comprises:

performing positive sequence sequencing on all train tasks according to task starting time to obtain a third train task list after positive sequence sequencing;

and distributing the first object to the train task which belongs to the second shift and the third shift in the third train task list according to the greedy positive sequence algorithm, wherein the state information of the first train task of the first object is the train task which is out of the field and out of the section, and obtaining a third distribution result, wherein the third distribution result comprises a third task amount distributed to the first object.

8. The method for allocating the first object according to claim 4, wherein calculating the state information of the first train task of the first object in the second shift and the third shift according to the greedy reverse order algorithm in a reverse order manner, and the state information is a fourth allocation result of the train task of the return section comprises the following steps:

sequencing all the train tasks in a reverse order according to the task ending time to obtain a fourth train task list after sequencing in the reverse order;

and distributing the first object to the train task of the return section of the train task in the fourth train task list according to the greedy reverse order algorithm to obtain a fourth distribution result, wherein the fourth distribution result comprises a fourth task amount distributed to the first object.

9. The method of claim 4, wherein the fifth assignment of the unassigned train task to the second shift and the third shift according to the second algorithm comprises:

performing positive sequence sequencing on the unallocated train tasks according to the starting time to obtain a fifth train task list subjected to positive sequence sequencing;

and allocating the first object to the train tasks belonging to the second shift and the third shift in the fifth train task list according to the second algorithm to obtain a fifth allocation result, wherein the fifth allocation result comprises a fifth task amount allocated to the first object.

10. The first object allocation method according to claim 4, wherein after allocating the first object to the train task having different status information based on the first algorithm and the second algorithm, the method comprises:

evaluating the distribution result of the first object by using an evaluation function MinF to obtain an evaluation function value, wherein the evaluation function MinF is as follows:

representing the average of the actual operating times of all said first objects, f ₂ (j) Is the number of the first object, f ₂ ＝m，f ₃ Representing the working efficiency of the first object, the working efficiency being determined according to the quotient of the task duration of the first object and the actual working duration of the first object, f ₃ (j)＝d _j /w _j And d represents a task time length of the first object.

11. A processing apparatus for a train task, comprising:

the system comprises a first determining module, a second determining module and a switching module, wherein the first determining module is used for dividing each train line according to transfer stations on each train line to obtain a plurality of train tasks, and any one train task in the plurality of train tasks is used for indicating a running task of a train from one transfer station to another transfer station;

a second determining module, configured to determine state information of each train task according to a start-stop station of each train task in the multiple train tasks, where the state information includes one of: returning to the field, exiting from the field and outputting, and aligning;

the allocation module is used for allocating a first object to the train task with different state information based on a first algorithm and a second algorithm, wherein the first algorithm at least comprises one of the following steps: a greedy forward algorithm and a greedy reverse algorithm, wherein the second algorithm at least comprises one of the following algorithms: greedy grid algorithm, tabu search algorithm and simulated annealing algorithm.

12. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to carry out the method of any one of claims 1 to 10 when executed.

13. An electronic device, comprising a memory having a computer program stored therein and a processor arranged to perform the method of any of claims 1 to 10 by means of the computer program.