CN115438898B

CN115438898B - Method and device for distributing first object, storage medium and electronic device

Info

Publication number: CN115438898B
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: 2023-05-26
Anticipated expiration: 2042-05-25
Also published as: CN115438898A; WO2023226324A1

Abstract

The invention provides a first object distribution method, a first object distribution device, 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 of the train tasks is used for indicating the running task of the train from one transfer station to another transfer station; determining state information of each train task according to start and stop stations of each train task in the plurality of train tasks; assigning a first object for the train mission of different status information based on a first algorithm and a second algorithm, wherein the first algorithm comprises: the greedy positive sequence algorithm and the greedy reverse sequence algorithm, and the second algorithm at least comprises one of the following steps: the greedy grid algorithm, the tabu search algorithm and the simulated annealing algorithm solve the problems that in the related art, when a single algorithm is used for distributing a first object, the distributed result is not ideal (such as unbalanced tasks, more drivers and more calculation time consumption).

Description

Method and device for distributing first object, storage medium and electronic device

Technical Field

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

Background

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

To overcome the above problems, in the related art, a scheduling plan automatically generated by an algorithm is generally used to allocate drivers, 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 only generate a scheduling plan in a mode of solving a global optimal solution under the condition of small data volume, and when the data volume is large, the optimization algorithm needs to consume huge time expenditure to obtain the scheduling plan, so that the time cost for generating the scheduling plan is greatly increased, and the process of allocating drivers is too long. While the heuristic algorithm does not need to consume a large amount of time to generate the scheduling plan, the neighborhood range of the algorithm of the heuristic algorithm is extremely large and discontinuous, and the phenomenon of algorithm non-convergence or algorithm premature and the like is extremely easy to cause when the scheduling plan is generated, so that a result with large deviation from the global optimal solution is obtained, and the distribution result to drivers is not ideal (for example, the tasks are not balanced enough, the number of drivers is more, and the calculation time is more). Because each algorithm inevitably has certain algorithm defects, the allocation result of the method for allocating drivers by using only a single algorithm is not ideal, and the operation efficiency and the service level of the train cannot be truly improved.

Therefore, in order to solve the problem that the first object is not distributed with an ideal result (such as unbalanced task, more drivers, and more time consuming calculation) when a single algorithm is used in the related art, no effective technical solution has been proposed yet.

Disclosure of Invention

The embodiment of the invention provides a first object distribution method, a first object distribution device, a storage medium and an electronic device, which at least solve the problem that the distribution result is not ideal (for example, the number of drivers is more) when a single algorithm is used for distributing a first object in the related technology.

According to an embodiment of the present invention, there is provided a method for allocating a first object, including: dividing each train line according to a transfer station on each train line to obtain a plurality of train tasks, wherein any 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 state information of each train mission according to a start-stop station of each train mission in the plurality of train missions, wherein the state information comprises one of the following: a return-to-field return section, a discharge-to-field discharge section and a positive line; assigning a first object to a train mission of different status information based on a first algorithm and a second algorithm, wherein the first algorithm comprises at least one of: a greedy positive sequence algorithm and a greedy reverse sequence algorithm, wherein the second algorithm at least comprises one of the following steps: a greedy grid algorithm, a tabu search algorithm, and a simulated annealing algorithm.

In an alternative exemplary embodiment, assigning a first object to a train mission of different status information based on a first algorithm and a second algorithm includes: determining a plurality of preconfigured 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 and 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 plurality of 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 train tasks with different state information under each algorithm flow; determining a preset number of target solutions from a 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 assigning the first object to the train mission of the different status information based on the first algorithm and the second algorithm, the method further comprises: acquiring a scheduling constraint condition of the plurality of train tasks, wherein the scheduling constraint condition comprises: shift dividing parameters, dining or rest parameters, shift workload parameters; and generating a shift 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 starting time or the task ending time of the train task in the first shift, the second shift and the third shift are different.

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

In an alternative exemplary embodiment, calculating, in reverse order 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-to-field section includes: sequencing all train tasks in a reverse order according to the task ending time to obtain a first train task list sequenced in the reverse order; and distributing the first object for the first train task of the first object in the first train task list according to the greedy reverse order algorithm, wherein the first train task belongs to the first shift, and the state information of the first train task of the first object is that the train task of the return-to-field return section distributes the first object to obtain a first distribution result, and the first distribution result comprises a first task amount distributed for the first object.

In an alternative exemplary embodiment, the calculating the second allocation result of the unassigned train task in the first shift according to the greedy positive-order algorithm positive-order includes: the unallocated train tasks are ordered in a positive sequence according to the task starting time, and a second train task list after the positive sequence ordering is obtained; and distributing the first object for the train task of the first object in the second train task list, wherein the first train task belongs to the train task of the first shift, according to the greedy positive sequence algorithm, so as to obtain a second distribution result, and the second distribution result comprises a second task amount distributed for the first object.

In an alternative exemplary embodiment, 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-order algorithm positive-order is a third allocation result of the train task of the outbound section, including: the unallocated train tasks are subjected to positive sequence sorting according to the task starting time, and a third train task list after positive sequence sorting is obtained; and distributing the first object to the first train task of the first object in the third train task list according to the greedy positive sequence algorithm, wherein the first train task of the first object belongs to the second shift and the third shift, and the state information of the first train task of the first object is the train task of the outgoing section, so as to obtain a third distribution result, and the third distribution result comprises a third task amount distributed for the first object.

In an alternative exemplary embodiment, calculating, in reverse order according to the greedy reverse order algorithm, that the state information of the first train task of the first object in the second shift and the third shift is a fourth allocation result of the train task of the return-to-field section includes: the unallocated train tasks are ordered in a reverse order according to the task ending time, and a fourth train task list after the reverse order ordering is obtained; and distributing the first object to the train task of the first object in the fourth train task list according to the greedy reverse order algorithm, wherein the first train task of the first object belongs to the second shift and the third shift, and the state information of the first train task of the first object is the train task of the return-to-field section, so as to obtain a fourth distribution result, and the fourth distribution result comprises a fourth task quantity distributed for the first object.

In an alternative exemplary embodiment, the fifth allocation result of the train task unallocated in the second shift and the third shift according to the second algorithm includes: the unallocated train tasks are ordered in a positive sequence according to the starting time, and a fifth train task list after the positive sequence ordering is obtained; and distributing the first object for the train task of the first object in the fifth train task list, wherein the first train task belongs to the second shift and the third shift, according to the second algorithm, so as to obtain a fifth distribution result, and the fifth distribution result comprises a fifth task amount distributed for the first object.

In an alternative exemplary embodiment, after assigning the first object to the train mission of different status information based on the first algorithm and the second algorithm, the method includes: and 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 ₁ (j)、f ₂ (j)、f ₃ (j) A sub-objective function, c, being the evaluation function MinF ₁ 、c ₂ 、c ₃ Respectively f is the ₁ (j)、f ₂ (j)、f ₃ (j) M is the number of the first objects, j is more than or equal to 0, and m is more than or equal to 0;

f ₁ (j) Representing the workload balance of a first object with a sequence number j, wherein the workload balance is represented by the variance of the actual working time length of the first object, and w represents the actual working time length of the first object,' >

Mean value f representing the actual working time of all the first objects ₂ (j) For the number of the first objects, f ₂ ＝m，f ₃ Representing the working efficiency of the first object, wherein the working efficiency is 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 D represents a task duration of the first object.

According to still another embodiment of the present invention, there is provided a processing apparatus for train tasks, including: the first determining module is used for dividing each train line according to a transfer station on each train line to obtain a plurality of train tasks, and any 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; the second determining module is used for determining state information of each train task according to a start-stop station of each train task in the plurality of train tasks, wherein the state information comprises one of the following steps: a return-to-field return section, a discharge-to-field discharge section and a positive line; the distribution module is used for distributing a first object for 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: a greedy positive sequence algorithm and a greedy reverse sequence algorithm, wherein the second algorithm at least comprises one of the following steps: a greedy grid algorithm, a tabu search algorithm, and a simulated annealing algorithm.

According to a further aspect of embodiments of the present invention, there is also provided a computer readable storage medium having a computer program stored therein, wherein the computer program is arranged to perform the method of any of the above when run.

According to yet 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 performs the method of any one of the above through the computer program.

According to the invention, through the embodiment, each train line is divided according to the transfer station on each train line, so that a plurality of train tasks are obtained, and any train task in the plurality of train tasks is used for indicating the running task of a train from one transfer station to another transfer station; determining state information of each train mission according to a start-stop station of each train mission in the plurality of train missions, wherein the state information comprises one of the following: a return-to-field return section, a discharge-to-field discharge section and a positive line; assigning a first object to a train mission of different status information based on a first algorithm and a second algorithm, wherein the first algorithm comprises at least one of: a greedy positive sequence algorithm and a greedy reverse sequence algorithm, wherein the second algorithm at least comprises one of the following steps: the greedy grid algorithm, the tabu search algorithm and the simulated annealing algorithm solve the problem that in the related art, when a single algorithm is used for distributing the first object, the distributed result is not ideal (for example, the number of drivers is high).

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 do not constitute a limitation on the invention. In the drawings:

fig. 1 is a hardware block diagram 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 diagram of train mission division according to an embodiment of the present invention;

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

FIG. 5 is a schematic diagram of a configuration algorithm combining flow in accordance with an embodiment of the present invention;

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

FIG. 7 is a schematic diagram of a reverse shift of train mission according to an embodiment of the invention;

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

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

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

FIG. 11 is a schematic diagram of a greedy reverse order algorithm in accordance with an embodiment of the invention;

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

FIG. 13 is a schematic diagram of a greedy grid algorithm according to an embodiment of the 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 flow diagram (one) of a method of assigning a first object according to an embodiment of the present invention;

FIG. 17 is a flow diagram (II) of a method of assigning a first object according to an embodiment of the present invention;

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

Detailed Description

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

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise 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 embodiments provided by the embodiments of the present invention may be executed in a computer terminal or similar computing device. Taking a computer terminal as an example, fig. 1 is a block diagram of a hardware structure of a computer terminal of a first object allocation method according to an embodiment of the present invention. As shown in fig. 1, the computer terminal may include one or more (only one is shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA) and a memory 104 for storing data, and in one exemplary embodiment, may also include a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the computer terminal described above. For example, a computer terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than the equivalent functions shown in FIG. 1 or more than the functions shown in FIG. 1.

The memory 104 may be used to store a computer program, for example, a software program of application software and a module, 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 the computer program stored in the memory 104, thereby performing various functional applications and data processing, that is, implementing the above-mentioned method. 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 may further include memory remotely located relative to the processor 102, which may be connected to the computer terminal via 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 a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, simply referred to as NIC) that can connect 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 configured to communicate with the internet wirelessly.

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 a method for allocating a first object according to an embodiment of the present invention, as shown in fig. 2, where the flowchart includes the following steps:

step S202, dividing each train line according to a transfer station on each train line to obtain a plurality of train tasks, wherein any 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;

in one embodiment, the process of obtaining a train mission is described in connection with FIG. 3. As shown in fig. 3, the horizontal axis represents the running time of a subway train (i.e., 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., broken line frame sections in fig. 3) according to the transfer station of the driver, and these train tasks may constitute a task list of the driver, i.e., a trip sheet.

Step S204, determining state information of each train task according to a start-stop station of each train task in the plurality of train tasks, wherein the state information comprises one of the following steps: a return-to-field return section, a discharge-to-field discharge section and a positive line;

the start-stop station of each train task comprises a start station and an end station of each train task. The method includes the steps that a starting station is a parking lot or a vehicle section, and state information of a train task is determined to be 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-to-field return section; the starting station and the ending station are all positive stations, and the state information of the train task is determined to be positive.

In one embodiment, the status information of the train mission may be described in connection with fig. 4, as shown in fig. 4, as the train travels from the a-car section to the B-station, the status information of the train mission is the outbound section. When the train runs from the E station to the parking lot, the state information of the train task is a return-to-station return section. When the train travels from station C to station E on route 2, the status information of the train mission is positive.

Step S206, a first object is allocated to the train task of different state information based on a first algorithm and a second algorithm, wherein the first algorithm at least comprises one of the following: a greedy positive sequence algorithm and a greedy reverse sequence algorithm, wherein the second algorithm at least comprises one of the following steps: a greedy grid algorithm, a tabu search algorithm, and a simulated annealing algorithm.

Through the embodiment, a plurality of train tasks are obtained by dividing each train line according to the transfer station on each train line, and any train task in the plurality of train tasks is used for indicating the running task of a train from one transfer station to another transfer station; determining state information of each train mission according to a start-stop station of each train mission in the plurality of train missions, wherein the state information comprises one of the following: a return-to-field return section, a discharge-to-field discharge section and a positive line; the first object is allocated to the train task with different state information based on the first algorithm and the second algorithm, and the first object can be allocated by using a plurality of algorithms simultaneously through algorithm combination of the first algorithm and the second algorithm.

In an alternative exemplary embodiment, before the first object is allocated to the train task with different status information based on the first algorithm and the second algorithm, the method further includes the following steps, including: acquiring a scheduling constraint condition of the plurality of train tasks, wherein the scheduling constraint condition comprises: shift dividing parameters, dining or rest parameters, shift workload parameters; and generating a shift 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 starting time or the task ending 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 for dividing shifts of a driver or shifts of train tasks in a plurality of train tasks, the dining or rest parameter is used for scheduling dining or rest time for the first object, and the shift workload parameter is used for restricting task duration of drivers in different shifts.

Wherein the shift dividing parameters include: firstly, selecting to divide the shift according to the time of the first task of a driver or to divide the shift of a working section according to the moment; then selecting to divide the shift according to the task starting time or divide the shift according to the ending time in the return-to-field section. If the 'time division shift according to the first task of the driver' is selected, the shift division parameter is used for dividing the shift of the driver; if "time is used to divide the shift of the work section" is selected, the shift dividing parameter is used to divide the shift of the train task.

Wherein the dining or resting parameters include: the method comprises a Chinese meal time range, a time length, a dinner time range, a time length, a dining place and a dining mode, wherein the dining mode comprises a point-of-arrival dining mode and a midway dining mode, wherein the point-of-arrival dining mode indicates that dining is scheduled when the Chinese meal or the dinner time range arrives at the dining place, and the midway dining mode indicates that a certain percentage (settable) of the workload reaching the upper limit arrives at the dining place. The shift workload parameters include: the upper and lower limits of driving duration, the upper and lower limits of working duration, the upper and lower limits of driving mileage and the upper and lower limits of number of working sections of each shift.

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

step S10: configuration combined algorithm flow (corresponding to the algorithm flow described above): the problem that the workload of a return-to-field return section (greedy reverse order algorithm) and a departure-to-departure section (greedy positive order algorithm) is unsaturated is solved by adopting a determining algorithm, and then a combination algorithm is realized by calculating the residual tasks by adopting an advanced algorithm, so that the problem that the distribution result is not ideal (for example, the number of drivers is more) when a single algorithm is used for distributing the first object is solved.

As shown in fig. 5, various algorithms can be accessed in the algorithm flow architecture to realize the algorithm combined flexible configuration. For example, in one embodiment, in the algorithm flow, a greedy reverse algorithm is used to assign a driver to a first train task state information of a first object (i.e., a driver) in a first shift for a train task of a return-to-field section, then a greedy positive algorithm is used to assign a driver to an unassigned train task in the first shift, a greedy positive algorithm is used to assign a driver to a first train task state information of a driver for a train task of a departure section in a second shift and a third shift, a greedy reverse algorithm is used to assign a driver to a first train task state information of a driver for a train task of a return-to-field section in the second shift and the third shift, and a greedy grid algorithm is used to assign a driver to an unassigned train task in the second shift and the third shift.

In one embodiment, the combining algorithm configures parameters for each flow node in the flow including: shift, number of passes type, algorithm order, upper head count, calculation time. The site parameters of the configuration flow node comprise: and (3) whether the return-to-live return section continues to connect tasks, the task connection reservation time and the task connection target station or not is determined by the lower limit and the upper limit of the transfer interval time of each station.

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 by the driver is 3 and the task state information of the task "7" is the return-to-field section, the task "1", the task "3", the task "5" are allocated to the driver 1, the task "2", the task "4" and the task "6" are allocated to the driver 2 in the positive sequence, and at this time, the task "7" is allocated to the driver 3, the task "7" return-to-field section is executed by the driver 3, so that the following task is not received, and the task amount of the driver 3 in the allocation result is unsaturated.

In one embodiment, a result of assigning drivers using a greedy reverse order algorithm in combination with a greedy forward order algorithm is illustrated in conjunction with FIG. 7. The principle of the greedy reverse algorithm scheduling is described in connection with fig. 7. As shown in fig. 7, assuming that the target task amount allocated by the driver is 3, the task state information of the task "7" is a return-to-field section, and a greedy reverse algorithm is adopted for scheduling, then the task "7", the task "5" and the task "3" are allocated to the driver 1 in reverse order, the task "1", the task "4" and the task "8" are allocated to the driver 2 in positive order, and at the moment, the task "2", the task "6" and the task "10" are allocated to the driver 3 in positive order. The task amount of all drivers in the distribution result is saturated.

Further, in the above embodiment, the problem that the driver is likely to have unsaturated workload in the return-to-field section, for example, the workload of the driver 3 in fig. 6, is assigned by using a separate greedy positive sequence algorithm. The greedy reverse order algorithm can solve the problem of unsaturated workload of the return-to-field return segment, for example, in fig. 7, the workload allocated to the driver 1 is saturated.

Step S20: configuration algorithm parameters: parameters such as shift, dining, rest and the like are configured and used as scheduling constraint conditions. As shown in fig. 8, the scheduling constraints include: shift dividing parameters, dining or rest parameters, shift workload parameters.

Wherein, the shift dividing parameters include: and selecting whether to divide the shift according to the time of the first task of the driver or the shift used for dividing the working section at the moment, wherein each station is a second shift task starting moment, and whether to return to the field and return to the section is a first shift ending moment of dividing the shift according to the task ending time.

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

The shift workload parameters include: the upper and lower limits of driving duration, the upper and lower limits of working duration, the upper and lower limits of driving mileage and the upper and lower limits of number of working sections of each shift.

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

step S901, determining the preconfigured K combined algorithm flows. Starting from algorithm flow 1, j=1, calculated one by one.

In step S902, the corresponding local solution is calculated by using the algorithm type of the jth algorithm flow.

Step S903, the local solution is returned.

In one embodiment, where the local solution for each algorithm includes multiple, a 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 combined into a target solution (corresponding to the allocation result of the first object to be allocated to the train task with different status information).

Step S905, executing the 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 is obtained. For example, the uncertainty algorithm returns 10 optimal solutions according to the objective function.

Through the steps, the local solutions are generated by sequentially carrying out calculation according to the algorithm flow, the target solutions are obtained by combining the local solutions, and the optimal 10 solutions are returned according to the target function, so that fewer people can be calculated in a shorter calculation time. As shown in table 1, the combined algorithm flow has fewer people and a medium calculation time compared with the calculation performed by using a single algorithm, and has more calculation time and less calculation time compared with the greedy positive-order algorithm and the greedy reverse-order algorithm.

TABLE 1

For example, the calculation time obtained by the greedy positive sequence algorithm, the greedy reverse sequence algorithm and the greedy grid algorithm is 2 seconds, 2 seconds and 2 minutes respectively. Specifically, only greedy positive sequence calculation is used, the calculation time is 2 seconds, and the number of people is 96; the calculation is carried out in reverse order only by greedy, the calculation time is 2 seconds, and the number of people is 101; the calculation time is 2 minutes and the number of people is 93 by using greedy grid calculation only. The typical flow calculation is carried out by adopting a combination algorithm, the calculation time is 1 minute and 10 seconds, and the number of people is 91.

Step S40: and selecting an optimal solution from the obtained optimal solution list. As shown in FIG. 10, the optimal solution list may display the returned solutions, browsable, and selectable solutions. The optimal solution list shows 10 solutions returned, and taking the first solution as an example, the objective function value is 114009560, the number of drivers is 114, the number of drivers allocated to the early shift (corresponding to the first shift) is 37, the number of drivers allocated to the white shift (corresponding to the second shift) is 40, the number of drivers allocated to the late shift (corresponding to the third shift) is 37, the equalization degree corresponding to the first solution is 95.60, and the sports rate is 71.05%.

According to the technical scheme, the algorithm flow is realized by combining the single algorithms, the advanced algorithm search space is reduced, the calculation time is greatly shortened, and the problems of balanced workload, high running rate, relatively small number of drivers, improved train task allocation efficiency, improved train task allocation quality, missed arrangement and longer calculation time consumption are solved by locally adopting the corresponding algorithm types aiming at different problems.

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

In an alternative exemplary embodiment, to better understand how to allocate the first object to the train task 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 reverse according to the greedy reverse order algorithm to be the first allocation result of the train task of the return-to-field section; calculating a second allocation result of the train tasks which are not allocated in the first shift according to the greedy positive sequence algorithm positive sequence; 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, wherein the state information is a third distribution result of the train task of the exiting section; calculating a fourth distribution result of the train task of the return-to-field section according to the greedy reverse order algorithm; calculating a fifth allocation result of the train tasks which are not allocated in the second shift and the third shift according to the second algorithm; and determining the sum of the first allocation result, the second allocation result, the third allocation result, the fourth allocation result and the fifth allocation result as the allocation result of the first object allocated to the train task with different state information.

In an alternative exemplary embodiment, the specific step of calculating, 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-to-field section includes: firstly, sequencing all train tasks in a reverse order according to task ending time to obtain a first train task list sequenced 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 of the return-to-field return section, and a first distribution result is obtained, and the first distribution result comprises a first task amount distributed for the first object.

In one embodiment, the process of assigning 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 step 1101, sorting all N train tasks in a reverse order according to the task ending time to obtain a result after the reverse order sorting, and starting to circularly solve the 1 st task in the result after the reverse order sorting, namely i=1, and distributing drivers one by one, wherein N is a positive integer greater than or equal to 1, and 1 is less than or equal to i and less than or equal to N.

Step S1102, finding a driver waiting at the ending station of the ith train mission;

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, if no, executing step S1107;

step S1104, determining one or more drivers waiting to be allocated at the ending 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 i is less than or equal to N, if yes, executing step S1102, otherwise, executing step S1109;

step S1107, judging whether the status information of the ith train task is a return-to-field return section, and whether the shift is the first shift, if yes, executing step S1108, if no, executing step S1105;

step S1108, determining that 1 new driver calls to the ending station, distributing the ith train task to the 1 new driver, and executing step S1105;

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

Through the steps, the greedy reverse order algorithm is combined to realize the technical scheme that the train tasks of which the state information of the first train task of the driver in the first shift is the return-to-field return section are distributed, the distribution result of the driver is obtained, and the number of drivers distributed to the train tasks and the task quantity of the drivers are determined.

In an alternative exemplary embodiment, the specific step of calculating the second allocation result of the unassigned train task in the first shift according to the greedy positive-order algorithm positive-order includes: firstly, carrying out positive sequence sequencing on the unassigned train tasks according to the task starting time to obtain a second train task list after positive sequence sequencing; and distributing the first object to the train task 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 one embodiment, the process of assigning the first object to the train task is described in conjunction with the greedy positive sequence algorithm principle in fig. 12, specifically as follows:

step S1201, performing positive sequence sequencing on the unassigned N2 train tasks according to the task start time, to obtain a result after positive sequence sequencing, and starting to circularly solve the 1 st task in the result after positive sequence sequencing, i.e. i=1, and assigning drivers one by one, wherein N2 is a positive integer greater than or equal to 1, and i is greater than or equal to 1 and less than or equal to N2.

Step S1202, finding a driver waiting for allocation at the start station of the ith train mission;

Step S1203, determining whether a driver waiting for allocation at the start station of the ith train mission is found, if yes, executing step S1204, if no, executing step S1207;

step S1204, determining one or more drivers waiting to be allocated at the start station, and allocating the ith train task to the driver with the longest waiting time;

step S1205, executing the program of i++;

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

step S1207, judging whether the shift of the ith train mission is the first shift, if yes, executing step S1208, if no, executing step S1205;

step S1208, determining that 1 new driver calls to the start station, distributing the ith train task to the 1 new driver, and executing step S1205;

step S1209, confirming that the train tasks after the positive sequence sequencing are distributed, and obtaining a result m2 distributed to the drivers, namely, the train tasks distributed to each driver.

Through the steps, the unassigned train tasks in the first shift are assigned by combining the greedy positive sequence algorithm, the assignment result of the drivers is obtained, and the number of drivers assigned to the train tasks and the technical scheme of the task quantity of the drivers are determined.

In an alternative exemplary embodiment, a fifth allocation result of the second shift and the train tasks unallocated in the third shift according to the second algorithm is provided, specifically: firstly, carrying out positive sequence sequencing on unassigned train tasks according to task starting time to obtain a fifth train task list after positive sequence sequencing; and distributing the first object to the trains belonging to the second class and the third class 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 one embodiment, the process of assigning the first object to the train task is described in conjunction with the greedy grid algorithm principle in fig. 13, which is specifically as follows:

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

step S1302, assuming that x drivers are sequentially allocated to the train mission positive sequence, and starting to circularly solve from x=0, wherein x is more than or equal to 0 and less than or equal to M1;

step S1303, task allocation is performed for x drivers based on greedy positive sequence algorithm principle;

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

Step S1305, obtaining a solution;

step S1306, executing the program of x++;

step S1307, judging whether x is less than or equal to M1, if yes, executing step S1303 to step S1306 in a circulating way, if no, executing step S1308;

step S1308, m1+1 solutions are obtained.

Step S1309, calculating the number of drivers to be M2 based on a greedy reverse order algorithm principle;

step 1310, assuming that y drivers are allocated to the train task in reverse order in sequence, starting to circularly solve from y=0, wherein y is more than or equal to 0 and less than or equal to M2;

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

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

step S1313, obtaining a solution;

step S1314, executing the y++ program;

step S1315, judging whether y is equal to or less than M2, if yes, executing step S1311 to step S1314 in a circulating way, and if no, executing step S1316;

step S1316, m2+1 species are obtained.

Step S1317, determining m1+m2+2 solutions based on the above steps.

In step S1318, an optimal solution among m1+m2+2 solutions is determined based on the objective function (equivalent to the evaluation function described above).

Based on the greedy grid algorithm principle, quick calculation can be realized, for example, the calculation time of one positive sequence or one negative sequence is approximately equal to 2 seconds, the greedy grid algorithm has unique solutions, namely, the greedy grid algorithm meets the thought of scheduling for a driver as the calculation result of each time, and has higher sports rate. The local optimal solution can be determined using the greedy mesh algorithm described above.

Optionally, in one embodiment, the process of assigning the first object to the train task is described in conjunction with the tabu search algorithm principle in fig. 14, which is specifically as follows:

step S1401, calculating the unassigned train tasks by using a greedy positive sequence algorithm, wherein the obtained solution is used as an initial solution of a tabu search algorithm;

step S1402, a driver 1 with an unqualified task amount and an unlocked task is randomly selected, and then an unlocked task a is randomly selected from the task set of the driver 1, wherein the unqualified task amount indicates that the actually allocated task amount is smaller than the preset allocated task amount.

Step S1403 generates a neighborhood range of task a.

The task a may be a train task from the station 1 to the station 2 between 9 points and 9 points for 30 minutes, and the neighborhood range of the task a includes tasks sent from the station 2 to other stations within 20 minutes after the 9 points and 30 minutes.

In step S1404, after the task a and the other tasks in the neighborhood of the task a are allocated, an optimal solution is obtained from the allocation results of the task a and the other tasks in the neighborhood of the task a by using the objective function. The task A driver and the task of the driver are locked.

Step S1405, determining whether the tasks are all in the locked state, if so, executing step S1406, otherwise, executing step S1402.

Step S1406, obtaining an optimal solution.

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

ΔT＝F(S′)-F(S)(1)，

wherein T represents the initial temperature, S represents the initial solution state, and after the iteration L times of each T value, a new solution S' is obtained. If DeltaT < 0, S 'is accepted as a new current solution, otherwise, S' is accepted as a new current solution with probability P. Wherein p=exp (- Δt/T).

In one embodiment, the process of assigning the first object to the train mission is described in conjunction with the simulated annealing algorithm principle in fig. 15, specifically as follows:

step S1501, calculating the unassigned train task by using a greedy positive sequence algorithm, wherein the obtained solution is used as an initial solution of a simulated annealing algorithm, and an objective function is calculated;

step S1502, two intersection tables are randomly acquired, and a task B and a task C are respectively taken out from the two intersection tables;

wherein the intersection table represents a set of tasks for the first object.

Step S1503, checking whether task B and task C can be exchanged according to the constraint condition, if yes, executing step S1504, and if no, executing step S1512.

If the starting sites of the task B and the task C are the same and belong to the interval time range, the task B and the task C are exchanged.

In step S1504, task B and task C are exchanged.

In step S1505, it is determined whether two intersection tables can be combined, wherein the number of drivers can be reduced after the intersection tables are combined. If yes, step S1506 is executed, and if no, step S1512 is executed.

In step S1506, the two intersection tables are combined, and the objective function value is calculated.

Step S1507, it is determined whether the objective function value becomes smaller, if so, step S1508 is performed, otherwise, step S1511 is performed.

Step S1508, the annealing operation is accepted.

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

Step S1510, determining whether the loop termination condition is satisfied, if yes, executing step S1513, i.e. taking the objective function value calculated in step S1506 as the optimal solution. Otherwise, step S1502 is performed.

Step S1511, accepting the annealing operation with a certain probability.

Step S1512, refusal of the acceptance of the annealing operation.

Step S1513, obtaining an optimal solution.

In an alternative exemplary embodiment, after the first object is allocated 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 using an evaluation function MinF, to obtain an evaluation function value, where the evaluation function MinF is as follows:

f ₁ (j) Representing the workload balance of a first object with a sequence number j, wherein the workload balance is represented by the variance of the actual working time length of the first object, and w represents the actual working time length of the first object,'>

In an alternative exemplary embodiment, the specific step of calculating, according to the greedy positive 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 allocation result of the train task of the outbound section includes: firstly, carrying out positive sequence sequencing on unassigned train tasks according to task starting time to obtain a third train task list after positive sequence sequencing; and distributing the first object for the train task belonging 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 of the outgoing section, so as to obtain a third distribution result, and the third distribution result comprises a third task amount distributed for the first object.

In one embodiment, the process of assigning the first object to the train mission is described with reference to fig. 16, specifically as follows:

step S1601, performing positive sequence sequencing on the unassigned N3 train tasks according to the task start time, to obtain a result after positive sequence sequencing, and starting to circularly solve the 1 st task in the result after positive sequence sequencing, i.e. i=1, and assigning drivers one by one, wherein N3 is a positive integer greater than or equal to 1, and i is greater than or equal to 1 and less than or equal to N3.

Step S1602, finding a driver waiting at a start station of an ith train mission;

step S1603, determining whether to find a driver waiting for allocation at the start station of the ith train mission, if yes, executing step S1604, if no, executing step S1607;

step S1604, determining one or more drivers waiting to be allocated at the start 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 i is equal to or less than N3, if yes, executing step S1602, otherwise, executing step S1609;

step S1607, judging whether the status information of the ith train task is the outgoing 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 calls to the start station, assigning the ith train task to the 1 new driver, and executing step S1605;

step S1609, confirming that the train tasks after the positive sequence sequencing are distributed, and obtaining a result m3 distributed to the drivers, namely, the train tasks distributed to each driver.

In an alternative 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 according to the greedy reverse order algorithm in reverse order is specifically that the fourth allocation result of the train task of the return-to-field section includes: firstly, sorting unassigned train tasks in reverse order according to task ending time to obtain a fourth train task list after sorting in reverse order; and distributing the first object for the train task belonging to the second shift and the third shift in the fourth 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-to-return section, and a fourth distribution result is obtained, and the fourth distribution result comprises a fourth task amount distributed for the first object.

In one embodiment, the process of assigning the first object to the train mission is described with reference to fig. 17, specifically as follows:

Step S1701, sorting unassigned N4 train tasks in reverse order according to task end time, obtaining a result after sorting in reverse order, and starting to circularly solve the 1 st task in the result after sorting in reverse order, i.e. i=1, and assigning drivers one by one, wherein N4 is a positive integer greater than or equal to 1, and i is greater than or equal to 1 and less than or equal to N4.

Step S1702, finding a driver waiting for allocation at the ending station of the ith train mission;

step S1703, determining whether a driver waiting for allocation at the end station of the ith train mission is found, if yes, executing step S1704, if no, executing step S1707;

step S1704, determining one or more drivers waiting to be allocated at the ending station, and allocating 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 yes, executing step S1702, otherwise, executing step S1709;

step S1707, judging whether the status information of the ith train task is a return-to-field return section, and if so, executing step S1708, and if not, executing step S1705;

step S1708, determining that 1 new driver calls to the ending station, distributing the ith train task to the 1 new driver, and executing step S1705;

Step S1709, confirming that the train tasks after the reverse sequencing are distributed, and obtaining a result m4 distributed to the drivers, namely, the train tasks distributed to each driver.

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

TABLE 2

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

The tabu search algorithm is low in calculation speed, the time required for completing one calculation is about 20 minutes, the calculation result close to the global optimal solution can be obtained, the sports car rate is low, and the number of required drivers is small.

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

According to the embodiment, the method and the system can be used for solving the problems that the manual shift has potential safety hazards, is long in time consumption, low in quality and poor in strain capacity by analyzing the shift characteristics and combining with the steps and experience of the manual shift, and the optimization method needs huge time expenditure, the heuristic method is extremely difficult to converge or early mature, the algorithm combination flow concept is introduced, the corresponding algorithm types are adopted to solve different problems locally, then the drivers are distributed according to the scheme of flow sequence combination, and therefore the purposes of positive sequence driver distribution of a greedy positive sequence algorithm, reverse sequence driver distribution of a greedy reverse sequence algorithm, driver distribution of a greedy grid algorithm, driver distribution of a tabu search algorithm and driver distribution of a simulated annealing algorithm are achieved.

From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform 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, and the device is used for implementing the foregoing embodiments and preferred embodiments, 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.

The embodiment of the invention also provides a processing device for train tasks, as shown in fig. 18, fig. 18 is a structural block diagram of the processing device for train tasks according to the embodiment of the invention, which comprises:

A first determining module 1802, configured to divide each train line according to a transfer station on each train line, to obtain a plurality of train tasks, where any one 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 mission according to a start-stop station of each train mission in the plurality of train missions, where the status information includes one of: a return-to-field return section, a discharge-to-field discharge section and a positive line;

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

By the device, a plurality of train tasks are obtained by dividing each train line according to the transfer station on each train line, 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, and state information of each train task is determined according to a start-stop station of each train task in the plurality of train tasks, wherein the state information comprises one of the following steps: a return-to-field return section, a discharge-to-field discharge section and a positive line; the first object is allocated to the train task with different state information based on the first algorithm and the second algorithm, so that the problem that the allocation result is not ideal (for example, the number of drivers is high) when the first object is allocated by using a single algorithm in the related technology is solved.

In an alternative exemplary embodiment, the allocation module 1806 is further configured to determine a plurality of algorithm flows configured in advance, 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 following: the first algorithm and 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 plurality of 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 train tasks with different state information under each algorithm flow; determining a preset number of target solutions from a 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, the processing device for train tasks further includes: the system comprises an acquisition module, a scheduling constraint condition acquisition module and a scheduling control module, wherein the acquisition module is used for acquiring the scheduling constraint conditions of the plurality of train tasks, and the scheduling constraint conditions comprise: shift dividing parameters, dining or rest parameters, shift workload parameters; and generating a shift 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 starting time or the task ending 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 for dividing the shifts of the train tasks in the plurality of train tasks, the dining or rest parameter is used for scheduling dining or rest time for the first object, and the shift workload parameter is used for restricting the task duration of different shifts.

Wherein the shift dividing parameters include: firstly, selecting to divide the shift according to the time of the first task of a driver or to divide the shift of a working section according to time; then selecting to divide the shift according to the task starting time or divide the shift according to the ending time in the return-to-field section. If the 'time division shift according to the first task of the driver' is selected, the shift division parameter is used for dividing the shift of the driver; if "time is used to divide the shift of the work section" is selected, the shift dividing parameter is used to divide the shift of the train task.

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

In an alternative exemplary embodiment, the allocation module 1806 is further configured to sort all train tasks in a reverse order according to the task end time, to obtain a first train task list after the reverse order sorting; 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 of the return-to-field return section, and a first distribution result is obtained, and the first distribution result comprises a first task amount distributed for the first object.

In an alternative exemplary embodiment, the allocation module 1806 is further configured to perform positive sequence ordering on the unallocated train tasks according to the task start time, to obtain a second train task list after positive sequence ordering; and distributing the first object to the train 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 alternative exemplary embodiment, the allocation module 1806 is further configured to perform positive sequence sorting on all train tasks according to the task start time, to obtain a third train task list after positive sequence sorting; and distributing the first object for the train task belonging 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 of the outgoing section, so as to obtain a third distribution result, and the third distribution result comprises a third task amount distributed for the first object.

In an alternative exemplary embodiment, the allocation module 1806 is further configured to sort all train tasks in a reverse order according to the task end time, to obtain a fourth train task list after the reverse order sorting; and distributing the first object to the trains belonging to the second shift and the third shift in the fourth 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 of the return-to-field return section, and a fourth distribution result is obtained, and the fourth distribution result comprises a fourth task amount distributed for the first object.

In an alternative exemplary embodiment, the allocation module 1806 is further configured to perform positive sequence ordering on the unallocated train tasks according to the task start time, to obtain a fifth train task list after positive sequence ordering; and distributing the first object to the trains belonging to the second class and the third class 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.

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

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

Alternatively, in the present embodiment, the above-described storage medium may be configured to store program code for performing the steps of:

s1, dividing each train line according to a transfer station on each train line to obtain a plurality of train tasks, wherein any 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 state information of each train mission according to a start-stop station of each train mission in the plurality of train missions, wherein the state information comprises one of the following: a return-to-field return section, a discharge-to-field discharge section and a positive line;

s3, distributing a first object for 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 positive sequence algorithm and a greedy reverse sequence algorithm, wherein the second algorithm at least comprises one of the following steps: a greedy grid algorithm, a tabu search algorithm, and a simulated annealing algorithm.

Alternatively, in the present embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments and optional implementations, and this embodiment is not described herein.

An embodiment of the invention also provides an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.

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

Alternatively, in the present embodiment, the above-described processor may be configured to execute the following steps by a computer program:

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

The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention should be included in the protection scope of the present invention.

Claims

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

dividing each train line according to a transfer station on each train line to obtain a plurality of train tasks, wherein any 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 state information of each train mission according to a start-stop station of each train mission in the plurality of train missions, wherein the state information comprises one of the following: a return-to-field return section, a discharge-to-field discharge section and a positive line;

assigning a first object to a train mission of different status information based on a first algorithm and a second algorithm, wherein the first algorithm comprises at least one of: a greedy positive sequence algorithm and a greedy reverse sequence algorithm, wherein the second algorithm at least comprises one of the following steps: the method comprises the steps of a greedy grid algorithm, a tabu search algorithm and a simulated annealing algorithm, wherein before a first object is distributed for train tasks with different state information based on a first algorithm and a second algorithm, the method further comprises the following steps:

acquiring a scheduling constraint condition of the plurality of train tasks, wherein the scheduling constraint condition comprises: shift dividing parameters, dining or rest parameters, shift workload parameters;

Generating a shift task based on the shift division 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 a task start time or a task end time of train tasks in the first shift, the second shift, and the third shift are different, wherein assigning a first object to the train tasks of different status information based on a first algorithm and a second algorithm comprises:

calculating a first allocation result of the train task of the return field return section according to the greedy reverse order algorithm;

calculating a second allocation result of the train tasks which are not allocated in the first shift according to the greedy positive sequence algorithm positive sequence;

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, wherein the state information is a third distribution result of the train task of the exiting section;

calculating a fourth distribution result of the train task of the return-to-field section according to the greedy reverse order algorithm;

Calculating a fifth allocation result of the train tasks which are not allocated in the second shift and the third shift according to the second algorithm;

and determining the sum of the first allocation result, the second allocation result, the third allocation result, the fourth allocation result and the fifth allocation result as the allocation result of the first object allocated to the train task with different state information.

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

determining a plurality of preconfigured 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 and 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 plurality of 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 train tasks with different state information under each algorithm flow;

Determining a preset number of target solutions from a 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.

3. The method of assigning a first object according to claim 1, wherein calculating, in reverse order, according to the greedy reverse order algorithm, a first assignment result of a train task of which status information of a first train task of the first object in a first shift is a return-to-field return segment includes:

sequencing all train tasks in a reverse order according to the task ending time to obtain a first train task list sequenced 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 of the return-to-field return section, and a first distribution result is obtained, and the first distribution result comprises a first task amount distributed for the first object.

4. The method of assigning a first object according to claim 1, wherein calculating a second assignment result of unassigned train tasks in the first shift in accordance with the greedy positive-order algorithm positive-order comprises:

The unallocated train tasks are ordered in a positive sequence according to the task starting time, and a second train task list after the positive sequence ordering is obtained;

and distributing the first object to the train 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.

5. The method of assigning the first object according to claim 1, wherein calculating the third assignment result of the first train task of the first object in the second and third shift times as the train task of the outbound section according to the greedy forward algorithm forward order includes:

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

and distributing the first object for the train task belonging 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 of the outgoing section, so as to obtain a third distribution result, and the third distribution result comprises a third task amount distributed for the first object.

6. The method of assigning a first object according to claim 1, wherein calculating a fourth assignment result of a train task of which status information of a first train task of the first object in a second shift and the third shift is a return-to-field return segment in reverse order according to the greedy reverse order algorithm includes:

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

and distributing the first object for the train task belonging to the second shift and the third shift in the fourth 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-to-return section, and a fourth distribution result is obtained, and the fourth distribution result comprises a fourth task amount distributed for the first object.

7. The method of assigning a first object according to claim 1, wherein a fifth assignment result of an unassigned train task in a second shift and the third shift according to the second algorithm includes:

the unallocated train tasks are ordered in a positive sequence according to the starting time, and a fifth train task list after the positive sequence ordering is obtained;

And distributing the first object to the train tasks belonging to the second class and the third class 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.

8. The method of assigning a first object according to claim 1, wherein after assigning the first object to the train task of different status information based on the first algorithm and the second algorithm, comprising:

and 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:

9. A train mission processing apparatus, comprising:

the first determining module is used for dividing each train line according to a transfer station on each train line to obtain a plurality of train tasks, and any 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;

the second determining module is used for determining state information of each train task according to a start-stop station of each train task in the plurality of train tasks, wherein the state information comprises one of the following steps: a return-to-field return section, a discharge-to-field discharge section and a positive line;

the distribution module is used for distributing a first object for 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: a greedy positive sequence algorithm and a greedy reverse sequence algorithm, wherein the second algorithm at least comprises one of the following steps: the train task processing device comprises a greedy grid algorithm, a tabu search algorithm and a simulated annealing algorithm, wherein the train task processing device further comprises: an acquisition module for

generating a shift 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 starting time or the task ending time of the train task in the first shift, the second shift and the third shift are different, wherein the distribution module is further used for

10. A computer-readable storage medium, characterized in that the storage medium has stored therein a computer program, wherein the computer program is arranged to execute the method of any of the claims 1 to 8 when run.

11. An electronic device comprising a memory and a processor, the memory having stored therein a computer program, the processor being arranged to perform the method of any of claims 1 to 8 by means of the computer program.