Route search device, route search method, and program

Abstract

A route finding device according to an embodiment of the present invention is provided with a processor and a storage unit that stores a plurality of assignment patterns corresponding to a plurality of parameter combinations assigned to a power supply vehicle and a plurality of locations. The processor is provided with a condition processing unit, a data analysis processing unit, and a data determination unit. The condition processing unit extracts an assignment pattern satisfying a given constraint condition from among the plurality of assignment patterns. The data analysis processing unit optimizes an objective function for the patrol pattern of the power supply vehicle to the plurality of locations, in accordance with the extracted assignment pattern. The data determination unit determines the patrol route for the power supply vehicle from the optimization result. The condition processing unit has a function for performing a first step for extracting assignment patterns satisfying a static first constraint condition, and a function for performing a second step for further extracting an assignment pattern satisfying a dynamic second constraint condition from among the assignment patterns that satisfy the first constraint condition.

Description

Technical Field

[0001] One aspect of the present invention relates to a route search device, a route search method, and a program.

Background Art

[0002] Communication infrastructure is required to operate stably even during disasters. In particular, securing power is an urgent task. Therefore, communication carriers are considering a system in which multiple power trucks are put on standby and the power trucks are circulated to the affected bases (such as power outage buildings). In order to quickly restore services during disasters, it is essential to calculate routes for circulating multiple power trucks to multiple bases. However, simply calculating the routes is not sufficient, and it is necessary to take into account various factors (parameters) such as the availability of the power truck drivers and the workers supplying power (such as the availability of driving and electrical work), or work patterns. This presents the aspect of a so-called NP-hard problem, and the number of power truck deployment (circulation) route patterns has increased explosively, so it has taken a very long time to obtain a solution. To solve this kind of difficulty, for example, the technology described in Non-Patent Document 1 is known.

Prior Art Documents

Non-Patent Documents

[0003]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the existing technology, an exhaustive search was made for the power vehicle deployment route to obtain an exact solution. For this reason, it was necessary to evaluate and compare each tour route for all tour patterns, and the amount of calculation tended to increase. Even when trying to obtain an optimal solution instead of an exact solution, there were many parameters that could be assigned to the power vehicle, and since it was necessary to calculate the evaluation value from the assignable patterns and determine the optimal solution, it took an enormous amount of time until the calculation was completed. This invention was made paying attention to the above circumstances, and aims to provide a technology capable of shortening the tour route search time.

Means for Solving the Problems

[0005] The route search device according to one aspect of this invention includes a storage unit that stores a plurality of assignment patterns corresponding to combinations of a plurality of parameters assigned to a power vehicle and a plurality of bases, and a processor. The processor includes a condition processing unit, a data analysis processing unit, and a data determination unit. The condition processing unit extracts an assignment pattern that satisfies a given constraint condition from the plurality of assignment patterns. The data analysis processing unit optimizes an objective function related to the tour pattern of the power vehicle to a plurality of bases for the extracted assignment pattern. The data determination unit determines the tour route of the power vehicle from the result of the optimization. The condition processing unit has a function of executing a first step of extracting an assignment pattern that satisfies a static first constraint condition, and a function of executing a second step of further extracting an assignment pattern that satisfies a dynamic second constraint condition from the assignment patterns that satisfy the first constraint condition.

Effects of the Invention

[0006] According to one aspect of this invention, it is possible to provide a technology capable of shortening the tour route search time.

Brief Description of the Drawings

[0007]

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[0008] Hereinafter, embodiments of the present invention will be described with reference to the drawings. <Configuration> FIG. 1 is a diagram showing an example of a system including a traveling route search device according to the first embodiment of the present invention. In FIG. 1, the traveling route search device 10 includes a processor 11, a storage 12, an interface unit 13, and a memory 14. That is, the traveling route search device 10 is a computer and is realized, for example, as a personal computer or a server computer.

[0009] The interface unit 13 is connected to the network 100 and can access, for example, the database 2 to obtain information such as disaster situations. Further, the interface unit 13 outputs the traveling pattern 3 generated by the traveling route search device 10 in response to a request from, for example, an operator of a vehicle distribution center.

[0010] The storage 12 is a non-volatile storage medium (block device) such as a HDD (Hard Disk Drive) or an SSD (Solid State Drive). The storage 12 stores the allocation pattern 12a in addition to basic programs such as an OS (Operating System) and a device driver, and programs for realizing the functions of the route search device 10.

[0011] The allocation pattern 12a stores a plurality of allocation patterns corresponding to combinations of a plurality of parameters assigned to a power vehicle that is a target of route search and a plurality of bases that are destinations of the power vehicle.

[0012] The memory 14 in FIG. 1 is, for example, a RAM (Random Access Memory), and stores the calculated tour pattern 14c by the processor 11 in addition to the program 14a loaded from the storage.

[0013] The processor 11 is an arithmetic unit such as a Central Processing Unit (CPU) or a Micro Processing Unit (MPU), and realizes its functions by the program loaded into the memory 14.

[0014] Incidentally, the processor 11 includes a condition processing unit 111, a data analysis processing unit 112, and a data determination unit 115 as functional blocks (program modules) according to the embodiment. These functional blocks are processing functions realized by the processor 11 executing instructions included in the program 14a. That is, the route search device 10 of the present invention can also be realized by a computer and a program. It is possible to record and distribute the program on a recording medium such as an optical medium. Alternatively, it is also possible to provide the program through a network.

[0015] The condition processing unit 111 extracts an allocation pattern that satisfies the given constraint conditions from the allocation pattern 12a stored in advance in the storage 12. Here, the constraint conditions are, for example, information given from the database 2 or the operator, such as the number of bases (such as buildings) that the power vehicle tours, the departure time of the power vehicle, the remaining power of the power vehicle, the remaining power of the base, the permitted working hours of the operator, or the feasible operations of the operator, etc.

[0016] The data analysis processing unit 112 optimizes the objective function regarding the tour pattern of the power vehicle to the plurality of bases for the allocation pattern extracted by the condition processing unit 111. Examples of the objective function are, for example, [making the power outage time of a specific building zero], [minimizing the power outage time overall], etc. A plurality of objective functions may be prepared for calculating the tour route.

[0017] The data determination unit 115 determines the tour route of the power vehicle from the result of the optimization by the data analysis processing unit 112.

[0018] <Function> FIG. 2 is a flowchart showing an example of the processing procedure of the processor 11 shown in FIG. 1. In the embodiment, the search for the tour pattern of the route of each power vehicle is treated as a scheduling problem for extracting the best solution (approximate solution / optimal solution). The genetic algorithm is well-known as a solution to this type of problem.

[0019] In FIG. 2, the processor 11 sets the objective function. (Block S1). Here, the evaluation target value in the optimization operation is set to, for example, (A). Next, the processor 11 sets the constraint conditions (Block S2).

[0020] Next, the processor 11 divides the allocation pattern 12a to be calculated into a plurality of patterns (for example, 1 to N) (block S3), and performs the processes (1 to N) for each pattern group in parallel (block S4). Block S3 is a process of extracting an allocation pattern that satisfies a constraint condition (first constraint condition) set in advance (STEP1). By this step, the allocation patterns that conflict with the first constraint condition are excluded, and the search space for the tour route is reduced.

[0021] In block S4, an algorithm that can perform the process independently, such as a genetic algorithm or a branch and bound method, is adopted (STEP2). The process of STEP2 is a process of further extracting an allocation pattern that satisfies the dynamic second constraint condition from the allocation patterns extracted in STEP1.

[0022] In block S4, when the process starts (sub-block S41), after a certain period of time has elapsed (sub-block S42), an evaluation value is extracted, and common parameters (for example, power outage time, working hours per person, etc.) are periodically checked while performing parallel processing (block S5). If a value better than the evaluation target value A is obtained (sub-block S43), the process continues and the process of sub-block S42 is repeated again.

[0023] If the evaluation value of the objective function is not better than the evaluation target value A as a specified value, that is, if the evaluation value is equal to or less than the default value, the process ends halfway (sub-block S44). Also, if all the processes of N = 1 are completed (sub-block S45), the evaluation value (A) is updated and the processing procedure proceeds to the next block S6. The process of block S4 is performed in parallel for the N allocation pattern groups divided in block S3, thereby promoting the shortening of the calculation time.

[0024] By performing the determination of allocatability in multiple steps, reduction of the search space (STEP1) is achieved. Also, while performing parallel processing, common parameters are periodically checked, and it is determined whether each allocation pattern is optimal (STEP2). By these two steps, the search space is reduced, and the overall processing time can be completed in a short time.

[0025] In block S6, a specified time is confirmed. If the specified time has elapsed, some processing results are output, for example, and displayed on a monitor or the like of the operator's terminal (FIG. 1) (block S7). When it is determined that all processing in block S6 has been completed, the process reaches the state of all processing completed (block S8) and the process is completed.

[0026] FIG. 3 is a diagram for explaining an example of a method for calculating a default time in block S6 of FIG. 2. For example, in a graph plotting an evaluation value with respect to the passage of time, the time (saturation time) at which the evaluation value saturates from past results can be obtained, and this time can be used as the specified time.

[0027] FIG. 4 is a flowchart showing an example of a processing procedure in block S3 of FIG. 2. After the processor 11 extracts the constraint parameters (block S11), it checks the number of patterns (block S12). As the number of patterns, for example, an infinite value can be set when an optimal solution (exact solution) is required. If an approximate solution is sufficient, a finite default value can be set. The default value can also be calculated, for example, from the amount of past constraint parameters and the number of patterns that output successful patterns.

[0028] Next, the processor 11 randomly assigns constraint parameters (block S13) and checks for contradictions from the results of the assignment (block S14). A contradiction refers to a contradiction such as when a type of circuit building (e.g., high voltage) is assigned to a type of power supply vehicle (e.g., low voltage only). That is, it is not possible to patrol a high-voltage building with a low-voltage type of power supply vehicle. By eliminating such contradictory combinations, it becomes possible to reduce the search space for the patrol route. Note that the process of block S14 can be implemented as a separate module, enabling flexible adaptation to increases and decreases in combinations.

[0029] If there is a contradiction (yes in block S15), the processing procedure returns to block S13 again. If there is no contradiction (no in block S15), the pattern obtained in block S13 is retained as an assignment pattern and stored in the storage 12 (block S16). The procedures of blocks S13 to S16 are repeated until the retained assignment pattern reaches N (block S17). If the retained assignment pattern reaches N, it is retained as all the assignment patterns (block S18).

[0030] Figure 5 is a flowchart showing an example of the processing procedure in block S4 of Figure 2. In block S4, optimization of the objective function based on a genetic algorithm is performed. In Figure 5, after the processor 11 determines the number of repeated generations (M) (block S21), it performs the process of STEP2 (Figure 2) (block S22). As a method for determining the number of generations, for example, it is possible to calculate it from the default value, the past amount of constraint parameters, and the number of patterns that output successful patterns.

[0031] Next, the processor 11 extracts parameter A (block S23). There can be multiple cases for the parameter extracted here. If all of the extracted parameter A is the highest (block S24), the processor 11 checks the number of iteration generations (block S27), and if the number of generations reaches M (block S28), the process is completed. In block S24, for example, regarding the parameter (power outage time), the power outage time = 0 hours is the highest.

[0032] On the other hand, regarding the parameter (working hours per person), the working hours per person = 6 hours is not the highest. Therefore, assuming there is a better pattern, the processor 11 randomly changes the "parameter that is not the highest". If there are multiple parameters that are not the highest, fix one of them and randomly change it.

[0033] If it is No in block S24, the processor 11 changes the parameter other than the highest as a crossover or mutation. In the genetic algorithm, the one-point crossover, two-point crossover, and mutation rate can be separately changed and can be handled as separate processes.

[0034] Next, the processor 11 determines the presence or absence of a past pattern (block S26), and if (none), the processing procedure returns to block S22. If the past pattern is (all present), the processing procedure reaches block S27.

[0035] <Effect> FIG. 6 and FIG. 7 are diagrams for explaining the effects obtained by the present embodiment. As shown in FIG. 6(a), in the existing technology, since it is necessary to assign many parameters to the power supply vehicle, calculate the evaluation value from the assignable patterns, and determine the optimal solution, there are cases where it takes an enormous amount of time.

[0036] In contrast, in the embodiment as shown in FIG. 6(b), the feasibility of allocation is processed in STEP1, and by pruning patterns that do not match the common parameters from the evaluation values among the allocable patterns at regular intervals, the processing time can be shortened to calculate the optimal (approximate) solution. FIG. 7 shows an example of the calculated approximate solution.

[0037] FIGS. 8 and 9 are diagrams for explaining specific examples of the calculated approximate solution. As shown in FIG. 8, in the tour route calculated according to the embodiment, for example, the shift of workers at a building (North Building) in the middle is set. This avoids the concentration of operation on one worker, and as shown in FIG. 9, the operation can be distributed among multiple workers for efficient processing. Therefore, the overall working time can also be completed in a short time. That is, by predicting each processing time and leveling the processing of the entire process, the processing completion time can be shortened.

[0038] As described above, in the embodiment, the tour route of the power vehicle is treated as a scheduling problem, and the number of allocation patterns (tour patterns) to be searched is reduced by excluding patterns where the conditions do not match ( "conflict points") [STEP1]. Next, at regular intervals, the tour patterns during the search are evaluated, and the search for patterns whose evaluation results (※parameter A) are below a certain value is interrupted, thereby reducing the number of tour patterns to be searched [STEP2]. By the processing of STEP1 and STEP2, the number of tour patterns to be searched can be significantly reduced, and the shortening of the search time by pruning can be promoted.

[0039] That is, in the embodiment, the search for the tour route is formulated as a scheduling problem of extracting the best solution (approximate solution / optimal solution) for each tour pattern of the power vehicle, and the feasibility of allocation is performed in multiple steps (STEP1, STEP2). Thereby, the search space can be reduced. Also, by periodically checking the common parameters while performing parallel processing, the search space can be further reduced, and the overall processing time required for the search of the tour route can be further shortened.

[0040] Accordingly, according to the embodiment, it is possible to provide a technology capable of shortening the traveling route search time. As a result, it becomes possible to quickly propose the deployment route of the power vehicle at the time of disaster occurrence and contribute to reducing the operation. It is also possible to contribute to strengthening the BCP (Business Continuity Plan).

[0041] Note that the present invention is not limited to the above embodiment as it is, and at the implementation stage, the components can be modified and embodied without departing from the gist thereof. Also, various inventions can be formed by appropriately combining a plurality of components disclosed in the above embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, components from different embodiments may be appropriately combined.

Explanation of Signs

[0042] 2... Database 3... Traveling pattern 10... Traveling route search device 11... Processor 12... Storage 12a... Allocation pattern 13... Interface section 14... Memory 14a... Program 14c... Traveling pattern 100... Network 111... Condition processing section 112... Data analysis processing section 115... Data determination section.

Claims

1. A storage unit that stores a plurality of allocation patterns corresponding to a combination of a plurality of parameters allocated to a power vehicle and a plurality of bases, and a processor, The processor, The processor includes, A condition processing unit that extracts an allocation pattern that satisfies a given constraint condition from the plurality of allocation patterns, A data analysis processing unit that optimizes an objective function related to a tour pattern of the power vehicle to the plurality of bases for the extracted allocation pattern, A data determination unit that determines a tour route of the power vehicle from the result of the optimization, The condition processing unit, A function of executing a first step of extracting an allocation pattern that satisfies a static first constraint condition, A route search device including a function of executing a second step of further extracting an allocation pattern that satisfies a dynamic second constraint condition from the allocation patterns that satisfy the first constraint condition.

2. The route search device according to claim 1, wherein the first step is a step of reducing a search space of the tour route by excluding an allocation pattern that conflicts with the first constraint condition.

3. The route search device according to claim 1, wherein the second step is a step of interrupting a search for a tour route for an allocation pattern whose evaluation value of the objective function is equal to or less than a predetermined value.

4. The route search device according to claim 1, wherein the data analysis processing unit optimizes the objective function based on a genetic algorithm.

5. The route search device according to claim 1, wherein the data analysis processing unit optimizes the objective function by a branch and bound method.

6. A tour route search method for generating a tour route of a power vehicle by a computer including a storage unit that stores a plurality of allocation patterns corresponding to a combination of a plurality of parameters allocated to the power vehicle and a plurality of bases, and a processor, An extraction process in which the processor extracts an allocation pattern that satisfies a given constraint condition from the plurality of allocation patterns, A process in which the processor optimizes an objective function related to a tour pattern of the power vehicle to the plurality of bases for the extracted allocation pattern, A process in which the processor determines a tour route of the power vehicle from the result of the optimization, The extraction process, A process in which the processor extracts an allocation pattern that satisfies a static first constraint condition, A route search method comprising a process in which the processor further extracts an allocation pattern that satisfies a dynamic second constraint condition from the allocation patterns that satisfy the first constraint condition.

7. A program for causing a computer to function as each part of the route search device according to any one of claims 1 to 5.

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