WO2018090778A1 - 用于利用遗传算法对配送路线进行优化的方法和系统 - Google Patents

用于利用遗传算法对配送路线进行优化的方法和系统 Download PDF

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WO2018090778A1
WO2018090778A1 PCT/CN2017/106670 CN2017106670W WO2018090778A1 WO 2018090778 A1 WO2018090778 A1 WO 2018090778A1 CN 2017106670 W CN2017106670 W CN 2017106670W WO 2018090778 A1 WO2018090778 A1 WO 2018090778A1
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route
routes
genetic algorithm
termination condition
route set
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French (fr)
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邱小红
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Beijing Jingdong Century Trading Co Ltd
Beijing Jingdong Shangke Information Technology Co Ltd
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Beijing Jingdong Century Trading Co Ltd
Beijing Jingdong Shangke Information Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping

Definitions

  • the present disclosure relates generally to route optimization and, more particularly, to a method and system for optimizing a delivery route using a genetic algorithm.
  • Route optimization is a major link in logistics and distribution. Correct and reasonable arrangement of distribution routes can effectively reduce transportation costs, save transportation time, and improve customer service levels. Therefore, there has been extensive research on the optimization of logistics distribution routes, but the research methods are limited to intelligent optimization algorithms or improved intelligent optimization algorithms.
  • intelligent optimization algorithm it is necessary to first establish the mathematical expression of the optimization problem, namely the objective function, and set the constraint conditions to solve by programming.
  • the objective function already has a standard expression
  • the system in reality is a complex dynamic system, which is difficult to describe using mathematical expressions. Even if the system can be described using mathematical expressions, some ideals are included. Thoughts, such as experience and averages, make it difficult to accurately consider the randomness and complexity of the actual situation.
  • a method for optimizing a delivery route using a genetic algorithm comprising: generating a first route set according to a geographic location corresponding to a site, wherein the first The route set includes N routes; calculating an evaluation index corresponding to each route in the first route set; determining whether a termination condition is satisfied, and when the termination condition is not satisfied, using a genetic algorithm pair from the first
  • the m routes with the highest ranked evaluation index selected in the route set are processed to generate Nm routes, and the Nm routes and the m routes with the highest ranking of the evaluation indicators are combined into a new route set, where m ⁇ N; and repeating the calculating step and the combining step as the first route set as the first route set until the termination condition is satisfied, and determining the route corresponding to the highest evaluation index when the termination condition is satisfied is optimized Distribution route.
  • the first set of routes is randomly generated.
  • the evaluation indicator is a time taken to deliver according to the corresponding route.
  • the termination condition is that the preset number of iterations is reached.
  • each of the generated N-m routes is obtained by crossing or mutating the m routes ranked highest in the evaluation index selected from the first route set by using a genetic algorithm.
  • the method when the genetic algorithm is used to rank the m routes with the highest ranking of the evaluation indicators selected from the first route set
  • the method includes: arbitrarily selecting two routes from the m routes and intersecting the two routes to generate a new route, and when utilizing a genetic algorithm pair from the first route set When the selected m ranking routes with the highest ranking index are mutated, the method includes: arbitrarily selecting one route from the m routes and mutating the one route to generate a new route.
  • the routes in the new route set are different.
  • a system for optimizing a delivery route using a genetic algorithm including a processor and a memory.
  • a processor configured to execute an instruction to: generate a first route set according to a geographic location corresponding to the site, wherein the first route set includes N routes; calculate and each of the first route set An evaluation index corresponding to the route; determining whether the termination condition is satisfied, and when the termination condition is not satisfied, using the genetic algorithm to process the m routes with the highest ranking of the evaluation indicators selected from the first route set to generate Nm a route, and combining the Nm route with the m route with the highest ranking of the evaluation index into a new route set, where m ⁇ N; and repeating the calculation step by using the new route set as the first route set And the combining step until the termination condition is satisfied, and the route corresponding to the highest evaluation index when the termination condition is satisfied is determined as the optimized delivery route.
  • a memory configured to store the instructions.
  • the first set of routes is randomly generated.
  • the evaluation indicator is a time taken to deliver according to the corresponding route.
  • the termination condition is that the preset number of iterations is reached.
  • each of the generated N-m routes is obtained by crossing or mutating the m routes ranked highest in the evaluation index selected from the first route set by using a genetic algorithm.
  • the processor is further configured to execute an instruction to: when m routes with the highest ranking of the evaluation indicators selected from the first route set are crossed by using a genetic algorithm, from the m routes Deselecting two routes arbitrarily and intersecting the two routes to generate a new route; and when mutating m routes with the highest ranking of evaluation indicators selected from the first route set by using a genetic algorithm, Any one of the m routes is selected and the one route is mutated to generate a new route.
  • the routes in the new route set are different.
  • a computer readable medium comprising instructions, when executed by a processor, to execute an operation comprising: generating a first set of routes based on a geographic location corresponding to a site, wherein The first route set includes N routes; calculating an evaluation index corresponding to each route in the first route set; determining whether a termination condition is satisfied, and when the termination condition is not satisfied, using a genetic algorithm
  • the m routes with the highest ranking of the evaluation indicators selected from the first route set are processed to generate Nm routes, and the Nm routes and the m routes with the highest ranking of the evaluation indicators are combined into a new route set. , where m ⁇ N; and the new route set
  • the calculating step and the combining step are repeatedly performed as the first route set until the termination condition is satisfied, and the route corresponding to the highest evaluation index when the termination condition is satisfied is determined as the optimized delivery route.
  • the first set of routes is randomly generated.
  • the evaluation indicator is a time taken to deliver according to the corresponding route.
  • the termination condition is that the preset number of iterations is reached.
  • each of the generated N-m routes is obtained by crossing or mutating the m routes ranked highest in the evaluation index selected from the first route set by using a genetic algorithm.
  • the processor is further configured to execute an instruction to: when m routes with the highest ranking of the evaluation indicators selected from the first route set are crossed by using a genetic algorithm, from the m routes Selecting two routes arbitrarily and intersecting the two routes to generate a new route, and when using the genetic algorithm to mutate the m routes with the highest ranking of the evaluation indicators selected from the first route set, Any one of the m routes is selected and the one route is mutated to generate a new route.
  • the routes in the new route set are different.
  • the present disclosure combines the system simulation technology and the genetic algorithm in the intelligent optimization algorithm to solve the route optimization problem in the logistics distribution.
  • System simulation technology can effectively simulate dynamic systems in reality, which is a good solution for complex systems with time plus space plus random variables.
  • Genetic algorithm is a computational model that simulates the natural evolution of Darwin's biological evolution theory and the biological evolution process of genetic mechanism. It is a method to search for optimal solutions by simulating natural evolutionary processes.
  • the combination of system simulation technology and intelligent optimization algorithm, using simulation model instead of mathematical expression and using optimization algorithm to solve avoids the limitations of static thinking on algorithm solving.
  • FIG. 1 schematically illustrates a flowchart of a method for optimizing a delivery route using a genetic algorithm, in accordance with an embodiment of the present disclosure
  • FIG. 2 is a flow chart that schematically illustrates a method of optimizing a delivery route by system simulation software, in accordance with an embodiment of the present disclosure
  • FIG. 3 schematically illustrates a schematic diagram of a delivery route optimization system in accordance with an embodiment of the present disclosure
  • FIG. 4 schematically illustrates a schematic diagram of a delivery model in accordance with an embodiment of the present disclosure
  • FIG. 5 schematically illustrates a block diagram of a system for optimizing a delivery route using a genetic algorithm, in accordance with an embodiment of the present disclosure.
  • the techniques of this disclosure may be implemented in the form of hardware and/or software (including firmware, microcode, etc.). Additionally, the techniques of the present disclosure may take the form of a computer program product on a computer readable medium having stored thereon instructions, the computer Program products are available for use by the instruction execution system or in conjunction with the instruction execution system.
  • a computer readable medium can be any medium that can contain, store, communicate, propagate or transport the instructions.
  • a computer readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium.
  • the computer readable medium include: a magnetic storage device such as a magnetic tape or a hard disk (HDD); an optical storage device such as a compact disk (CD-ROM); a memory such as a random access memory (RAM) or a flash memory; and/or a wired /Wireless communication link.
  • a magnetic storage device such as a magnetic tape or a hard disk (HDD)
  • an optical storage device such as a compact disk (CD-ROM)
  • a memory such as a random access memory (RAM) or a flash memory
  • RAM random access memory
  • FIG. 1 schematically illustrates a flow diagram of a method 100 for optimizing a delivery route using a genetic algorithm, in accordance with an embodiment of the present disclosure.
  • a first route set is generated according to a geographic location corresponding to the site, wherein the first route set includes N routes.
  • the first set of routes can be randomly generated.
  • step S102 evaluation indicators corresponding to the respective routes in the first route set are calculated.
  • the evaluation indicator may be the time taken to deliver according to the corresponding route.
  • step S103 it is determined whether the termination condition is satisfied, and when the termination condition is not satisfied, the m-routes with the highest ranking of the evaluation indicators selected from the first route set are processed by the genetic algorithm to generate Nm routes, and the Nm routes are The m route with the highest ranking of the evaluation index is combined into a new route set, where m ⁇ N.
  • the termination condition can be to reach a preset number of iterations.
  • Each of the generated N-m routes is obtained by crossing or mutating the m routes ranked highest among the evaluation indicators selected from the first route set by using a genetic algorithm.
  • the method 100 includes: arbitrarily selecting two routes from the m routes and crossing the two routes To generate a new route, and when mutating the m-routes with the highest ranking of the evaluation indicators selected from the first set of routes using a genetic algorithm, the method 100 includes: arbitrarily selecting a route from the m routes and The one route is mutated to create a new route.
  • the m-routes with the highest ranking of the evaluation indicators are processed by the genetic algorithm to generate N-m routes, there may be duplicate routes in the generated N-m routes.
  • the route selected in the m route with the highest ranking of the evaluation index is crossed or mutated to generate a route, The newly generated route is compared with the route that has been generated using the genetic algorithm, and if the comparison result shows that there is a route in the route generated by the genetic algorithm that is the same as the newly generated route, the newly generated route is discarded, and the genetic algorithm is utilized. Regenerate the route until there are no duplicate routes.
  • step S104 the calculation step and the combining step are repeatedly performed as the first route set as the first route set until the termination condition is satisfied, and the route corresponding to the highest evaluation index when the termination condition is satisfied is determined to be optimized Delivery route.
  • a method for optimizing a delivery route using a genetic algorithm in accordance with an embodiment of the present disclosure will be described below with reference to FIG. 2 in conjunction with a specific system simulation software.
  • a simulation model library is established.
  • the logistics system simulation software such as flexsim, Automod, etc., can be used to establish a distribution route optimization system, as shown in FIG. 3, including a simulation model library, a tree node required to write a genetic algorithm, and a graphical user interface (GUI) of the model control interface.
  • GUI graphical user interface
  • the simulation model library When constructing the simulation model library, first draw the required 3D map, for example, draw the Chinese map by province and import it into the simulation software; then build the entities needed to simulate the model, such as the entity that generates the delivery vehicle, the entity of the distribution site, And save these entities as a component library.
  • the model control interface GUI is a function of the simulation software. It only needs to drag the existing control into the GUI interface for layout, and then link with the model library and the genetic algorithm parameter node.
  • the site is set. Enter the number of delivery sites in the Layout tab of the GUI interface. For example, set up 10 delivery sites, output the delivery site table, and enter the city name of a delivery site in each row of the table.
  • step S203 the distance between the stations is set.
  • the Layout tab of the GUI interface click on the Set Distances table and enter the distance between the two sites in the pop-up form.
  • a specific delivery route model is generated.
  • a specific simulation model is generated using the entities in the simulation model library, as shown in FIG. 4, a red five-pointed star is a distribution site, and there is a distribution between any two distribution sites.
  • step S205 related parameters of the genetic algorithm are set.
  • Enter parameters in the Genetic Algorithm tab of the GUI interface including route set size, number of iterations, selection, crossover, and mutation.
  • the route set here is a feasible solution set of the distribution route optimization problem.
  • the number of solutions in the solution set that is, the number of distributions is the size of the route set, and one solution or one distribution route is called an individual.
  • the number of iterations is used as a condition for termination of the genetic algorithm.
  • Selection, crossover, and mutation are the basics of genetic algorithms.
  • An operator that is used to generate a new set of routes during the iteration.
  • the selection is the operation of picking out the winning individual from the current route set and retaining it to the new route set.
  • the crossover and mutation are the reorganization of the individuals in the current route set, thereby obtaining the operation of the new individual, picking out the winning individual and recombining the obtained
  • the new individuals form a new collection of routes.
  • the route set is initialized.
  • a set of routes is randomly generated using the Initialize population subnode in the genetic algorithm tree node.
  • the process is as follows: Number the entered sites. For example, as described above, when 10 stations are input in step S202, the stations are numbered by an integer between 1 and 10, and the station number is randomly scrambled to obtain a sequence of numbers, which represents a delivery, also It can be called an individual, randomly generating a set of sizes and individuals, and forming an initial set of routes.
  • step S207 the delivery route model generated in step S204 is executed to obtain an evaluation index.
  • the individual is used as the input of the simulation model, the simulation model is run, and the resulting delivery time is used as an indicator to evaluate the individual's quality.
  • step S208 it is judged whether the current route set is running or not. If not, the process proceeds to step S207, and if it is completed, step S209 is performed.
  • step S209 it is judged whether or not the termination condition is satisfied.
  • the termination condition here is that the number of iterations reaches the set target value, if the termination condition is not satisfied, step S210 is performed, and if the termination condition is satisfied, step S211 is performed.
  • step S210 the new route set model is run to obtain an evaluation index and jumps to step S207.
  • the new route set is generated by using the generate_next_population sub-node in the genetic algorithm tree node, and the process includes: retaining a certain amount of winning individuals by selecting operators, and then recombining through crossover and mutation operators to obtain some new individuals, thereby composing New route collection.
  • step S211 the running of the delivery route model is stopped to obtain a delivery route.
  • the delivery route is the route with the highest evaluation index in the current route collection.
  • the number of times the simulation model runs is equal to the route collection size multiplied by the number of iterations.
  • the last run route is the optimized delivery route.
  • the technical solution of the present disclosure avoids solving the dynamic problem with static thinking, and the operation is simple, and only by inputting the number of distribution sites, the name of the city where the site is located, and the distance between the two sites, it can be determined through simulation experiments. An optimized delivery.
  • FIG. 5 schematically illustrates a block diagram of a system 500 for utilizing a genetic algorithm to optimize a delivery route, in accordance with an embodiment of the present disclosure.
  • System 500 includes a processor 510, such as a digital signal processor (DSP).
  • DSP digital signal processor
  • Processor 510 can be a single device or multiple devices for performing the different acts of method 100 described herein.
  • the processor 510 is configured to execute an instruction to: generate a first route set according to a geographic location corresponding to the site, wherein the first The route set includes N routes; calculating an evaluation index corresponding to each route in the first route set; determining whether a termination condition is satisfied, and when the termination condition is not satisfied, using a genetic algorithm pair from the first
  • the m routes with the highest ranked evaluation index selected in the route set are processed to generate Nm routes, and the Nm routes and the m routes with the highest ranking of the evaluation indicators are combined into a new route set, where m ⁇ N And repeating the calculating step and the combining step as the first route set as the first route set until the termination condition is satisfied, and determining the route corresponding to the highest evaluation index when the termination condition is satisfied as the optimized delivery route.
  • the processor 510 may be further configured to execute an instruction to: arbitrarily select from the m routes when a m-route with the highest ranking of the evaluation indicators selected from the first route set is crossed by a genetic algorithm Two routes intersecting the two routes to generate a new route, and when m routes with the highest ranking of evaluation indicators selected from the first route set are mutated by a genetic algorithm, from the m A route is arbitrarily selected and the route is mutated to create a new route.
  • System 500 can also include input/output (I/O) devices 530 for receiving signals from other entities or for transmitting signals to other entities.
  • I/O input/output
  • system 500 includes a memory 520 that can have the form of a non-volatile or volatile memory, such as an electrically erasable programmable read only memory (EEPROM), flash memory, or the like.
  • Memory 520 stores computer readable instructions that, when executed by processor 510, cause the processor to perform the actions described herein.
  • EEPROM electrically erasable programmable read only memory
  • the techniques of this disclosure may be implemented in the form of hardware and/or software (including firmware, microcode, etc.). Additionally, the techniques of this disclosure may take the form of a computer program product on a computer readable medium storing instructions for use by or in connection with an instruction execution system (eg, one or more processors) .
  • a computer readable medium can be any medium that can contain, store, communicate, propagate or transport the instructions.
  • a computer readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium.
  • the computer readable medium include: a magnetic storage device such as a magnetic tape or a hard disk (HDD); an optical storage device such as a compact disk (CD-ROM); a memory such as a random access memory (RAM) or a flash memory; and/or a wired /Wireless communication link.
  • a magnetic storage device such as a magnetic tape or a hard disk (HDD)
  • an optical storage device such as a compact disk (CD-ROM)
  • a memory such as a random access memory (RAM) or a flash memory
  • RAM random access memory
  • aspects of the embodiments disclosed herein may be implemented in an integrated circuit as a whole or in part, as one or more of one or more computers running on one or more computers.
  • a computer program eg, implemented as one or more programs running on one or more computer systems
  • implemented as one or more programs running on one or more processors eg, implemented as one or One or more programs running on a plurality of microprocessors, implemented as firmware, or substantially in any combination of the above, and those skilled in the art, in accordance with the present disclosure, will be provided with design circuitry and/or write software and / or firmware code capabilities.
  • signal bearing media include, but are not limited to, recordable media such as floppy disks, hard drives, compact disks (CDs), digital versatile disks (DVDs), digital tapes, computer memories, and the like; and transmission-type media such as digital and / or analog communication media (eg, fiber optic cable, waveguide, wired communication link, wireless communication link, etc.).

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Abstract

一种用于利用遗传算法对配送路线进行优化的方法，包括：根据与站点相对应的地理位置来生成第一路线集合，其中，所述第一路线集合包括N条路线（S101）；计算与所述第一路线集合中的各条路线相对应的评价指标（S102）；确定是否满足终止条件，当不满足所述终止条件时，利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行处理以产生N-m条路线，并且将所述N-m条路线与所述评价指标排名最高的m条路线组合为新的路线集合，其中m＜N（S103）；以及将所述新的路线集合作为第一路线集合重复执行所述计算步骤和所述组合步骤，直到满足终止条件为止，并且将满足终止条件时与最高评价指标相对应的路线确定为优化的配送路线（S104）。

Description

用于利用遗传算法对配送路线进行优化的方法和系统 技术领域
本公开大体上涉及路线优化，更具体地，涉及用于利用遗传算法对配送路线进行优化的方法和系统。
背景技术
路线优化是物流配送中的一个主要环节，正确合理地安排配送路线可以有效地降低运输成本，节约运输时间，并提高客户服务水平。因此对于物流配送路线的优化问题，已经有了很广泛的研究，但是研究方法局限于智能优化算法或者改进的智能优化算法。当采用智能优化算法进行求解时，需要首先建立优化问题的数学表达式，即目标函数，并设定约束条件，通过编程进行求解。虽然该目标函数已经有了标准的表达式，但是现实情况中的系统是一个复杂的动态系统，很难使用数学表达式进行描述，即使能够使用数学表达式对系统进行描述，也包含了一些理想化的思想，比如经验、平均值，难以精确地考虑到实际情况中的随机性和复杂性。
发明内容
鉴于此，在本公开的一个方面，提出了一种用于利用遗传算法对配送路线进行优化的方法，包括：根据与站点相对应的地理位置来生成第一路线集合，其中，所述第一路线集合包括N条路线；计算与所述第一路线集合中的各条路线相对应的评价指标；确定是否满足终止条件，当不满足所述终止条件时，利用遗传算法对从所述第一路线集合中选择出的评价指标排名最高的m条路线进行处理以产生N-m条路线，并且将所述N-m条路线与所述评价指标排名最高的m条路线组合为新的路线集合，其中m＜N；以及将所述新的路线集合作为第一路线集合重复执行所述计算步骤和所述组合步骤，直到满足终止条件为止，并且将满足终止条件时与最高评价指标相对应的路线确定为优化的配送路线。
可选地，所述第一路线集合是随机产生的。
可选地，所述评价指标是按照相应路线进行配送所花费的时间。
可选地，所述终止条件是达到预先设置的迭代次数。
可选地，所产生的N-m条路线中的每一条路线是通过利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行交叉或变异获得的。
可选地，当利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线 进行交叉时，所述方法包括：从所述m条路线中任意选择两条路线并对所述两条路线进行交叉以产生一条新路线，以及当利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行变异时，所述方法包括：从所述m条路线中任意选择一条路线并对所述一条路线进行变异以产生一条新路线。
可选地，所述新的路线集合中的路线各不相同。
在本公开的另一个方面，提出了一种用于利用遗传算法对配送路线进行优化的系统，包括处理器和存储器。处理器，被配置为执行指令以：根据与站点相对应的地理位置来生成第一路线集合，其中，所述第一路线集合包括N条路线；计算与所述第一路线集合中的各条路线相对应的评价指标；确定是否满足终止条件，当不满足所述终止条件时，利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行处理以产生N-m条路线，并且将所述N-m条路线与所述评价指标排名最高的m条路线组合为新的路线集合，其中m＜N；以及将所述新的路线集合作为第一路线集合重复所述计算步骤和所述组合步骤，直到满足终止条件为止，并且将满足终止条件时与最高评价指标相对应的路线确定为优化的配送路线。存储器，被配置为存储所述指令。
可选地，所述第一路线集合是随机产生的。
可选地，所述评价指标是按照相应路线进行配送所花费的时间。
可选地，所述终止条件是达到预先设置的迭代次数。
可选地，所产生的N-m条路线中的每一条路线是通过利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行交叉或变异获得的。
可选地，所述处理器被进一步配置为执行指令以：当利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行交叉时，从所述m条路线中任意选择两条路线并对所述两条路线进行交叉以产生一条新路线；以及当利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行变异时，从所述m条路线中任意选择一条路线并对所述一条路线进行变异以产生一条新路线。
可选地，所述新的路线集合中的路线各不相同。
在本公开的另一个方面，提出了一种计算机可读介质，包括在由处理器执行时执行包括以下各项的操作的指令：根据与站点相对应的地理位置来生成第一路线集合，其中，所述第一路线集合包括N条路线；计算与所述第一路线集合中的各条路线相对应的评价指标；确定是否满足终止条件，当不满足所述终止条件时，利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行处理以产生N-m条路线，并且将所述N-m条路线与所述评价指标排名最高的m条路线组合为新的路线集合，其中m＜N；以及将所述新的路线集合 作为第一路线集合重复执行所述计算步骤和所述组合步骤，直到满足终止条件为止，并且将满足终止条件时与最高评价指标相对应的路线确定为优化的配送路线。
可选地，所述第一路线集合是随机产生的。
可选地，所述评价指标是按照相应路线进行配送所花费的时间。
可选地，所述终止条件是达到预先设置的迭代次数。
可选地，所产生的N-m条路线中的每一条路线是通过利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行交叉或变异获得的。
可选地，所述处理器被进一步配置为执行指令以：当利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行交叉时，从所述m条路线中任意选择两条路线并对所述两条路线进行交叉以产生一条新路线，以及当利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行变异时，从所述m条路线中任意选择一条路线并对所述一条路线进行变异以产生一条新路线。
可选地，所述新的路线集合中的路线各不相同。
本公开将系统仿真技术和智能优化算法中的遗传算法结合在一起，解决了物流配送中的路线优化问题。系统仿真技术可以有效地模拟现实中的动态系统，对于时间加空间加随机变量的复杂系统是一种很好的解决方法。遗传算法是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法。将系统仿真技术和智能优化算法结合起来，使用仿真模型代替数学表达式并且使用优化算法进行求解，从而避免了静态思维对于算法求解的局限性。
附图说明
根据结合示例性附图对示例性实施例的以下描述，本公开的其他细节、方面和优点将变得显而易见，在附图中：
图1示意性地示出了根据本公开的实施例的用于利用遗传算法对配送路线进行优化的方法的流程图；
图2示意性地示出了根据本公开的实施例的通过系统仿真软件对配送路线进行优化的方法的流程图；
图3示意性地示出了根据本公开的实施例的配送路线优化系统的示意图；
图4示意性地示出了根据本公开的实施例的配送模型的示意图；以及
图5示意性地示出了根据本公开的实施例的用于利用遗传算法对配送路线进行优化的系统的框图。
附图没有对实施例的所有电路或结构进行显示。贯穿所有附图相同的附图标记表示相同或相似的部件或特征。
具体实施方式
下面将详细描述本公开的具体实施例，应当注意，这里描述的实施例只用于举例说明，并不用于限制本公开。在以下描述中，为了提供对本公开的透彻理解，阐述了大量特定细节。然而，对于本领域普通技术人员显而易见的是：不必采用这些特定细节来实行本公开。在其他实例中，为了避免混淆本公开，未具体描述公知的电路、材料或方法。
在此使用的术语仅仅是为了描述具体实施例，而并非意在限制本公开。在此使用的术语“包括”、“包含”等表明了所述特征、步骤、操作和/或部件的存在，但是并不排除存在或添加一个或多个其他特征、步骤、操作或部件。
在此使用的所有术语(包括技术和科学术语)具有本领域技术人员通常所理解的含义，除非另外定义。应注意，这里使用的术语应解释为具有与本说明书的上下文相一致的含义，而不应以理想化或过于刻板的方式来解释。
在使用类似于“A、B和C等中至少一个”这样的表述的情况下，一般来说应该按照本领域技术人员通常理解该表述的含义来予以解释(例如，“具有A、B和C中至少一个的系统”应包括但不限于单独具有A、单独具有B、单独具有C、具有A和B、具有A和C、具有B和C、和/或具有A、B、C的系统等)。在使用类似于“A、B或C等中至少一个”这样的表述的情况下，一般来说应该按照本领域技术人员通常理解该表述的含义来予以解释(例如，“具有A、B或C中至少一个的系统”应包括但不限于单独具有A、单独具有B、单独具有C、具有A和B、具有A和C、具有B和C、和/或具有A、B、C的系统等)。本领域技术人员还应理解，实质上任意表示两个或更多可选项目的转折连词和/或短语，无论是在说明书、权利要求书还是附图中，都应被理解为给出了包括这些项目之一、这些项目任一方、或两个项目的可能性。例如，短语“A或B”应当被理解为包括“A”或“B”、或“A和B”的可能性。
附图中示出了一些方框图和/或流程图。应理解，方框图和/或流程图中的一些方框或其组合可以由计算机程序指令来实现。这些计算机程序指令可以提供给通用计算机、专用计算机或其他可编程数据处理装置的处理器，从而这些指令在由该处理器执行时可以创建用于实现这些方框图和/或流程图中所说明的功能/操作的装置。
因此，本公开的技术可以硬件和/或软件(包括固件、微代码等)的形式来实现。另外，本公开的技术可以采取存储有指令的计算机可读介质上的计算机程序产品的形式，该计算机 程序产品可供指令执行系统使用或者结合指令执行系统使用。在本公开的上下文中，计算机可读介质可以是能够包含、存储、传送、传播或传输指令的任意介质。例如，计算机可读介质可以包括但不限于电、磁、光、电磁、红外或半导体系统、装置、器件或传播介质。计算机可读介质的具体示例包括：磁存储装置，如磁带或硬盘(HDD)；光存储装置，如光盘(CD-ROM)；存储器，如随机存取存储器(RAM)或闪存；和/或有线/无线通信链路。
在整个说明书中，对“一个实施例”、“实施例”、“一个示例”或“示例”的提及意味着：结合该实施例或示例描述的特定特征、结构或特性被包含在本公开至少一个实施例中。因此，在整个说明书的各个地方出现的短语“在一个实施例中”、“在实施例中”、“一个示例”或“示例”不一定都指同一实施例或示例。此外，可以以任何适当的组合和/或子组合将特定的特征、结构或特性组合在一个或多个实施例或示例中。此外，本领域普通技术人员应当理解，在此提供的附图都是为了说明的目的，并且附图不一定是按比例绘制的。这里使用的术语“和/或”包括一个或多个相关列出的项目的任何和所有组合。
图1示意性地示出了根据本公开的实施例的用于利用遗传算法对配送路线进行优化的方法100的流程图。
在步骤S101，根据与站点相对应的地理位置来生成第一路线集合，其中，第一路线集合包括N条路线。第一路线集合可以是随机产生的。
在步骤S102，计算与第一路线集合中的各条路线相对应的评价指标。评价指标可以是按照相应路线进行配送所花费的时间。
在步骤S103，确定是否满足终止条件，当不满足终止条件时，利用遗传算法对从第一路线集合中选择的评价指标排名最高的m条路线进行处理以产生N-m条路线，并且将N-m条路线与所述评价指标排名最高的m条路线组合为新的路线集合，其中m＜N。终止条件可以是达到预先设置的迭代次数。所产生的N-m条路线中的每一条路线是通过利用遗传算法对从第一路线集合中选择的评价指标排名最高的m条路线进行交叉或变异获得的。
当利用遗传算法对从第一路线集合中选择的评价指标排名最高的m条路线进行交叉时，方法100包括：从所述m条路线中任意选择两条路线并对所述两条路线进行交叉以产生一条新路线，以及当利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行变异时，方法100包括：从所述m条路线中任意选择一条路线并对所述一条路线进行变异以产生一条新路线。
应当注意的是，当利用遗传算法对评价指标排名最高的m条路线进行处理以产生N-m条路线时，所产生的N-m条路线中可能存在重复的路线。为了产生N-m条各不相同的路线，每当对评价指标排名最高的m条路线中所选择的路线进行交叉或变异以产生一条路线时，将 新产生的路线与已经利用遗传算法产生的路线进行比较，如果比较结果显示已经利用遗传算法产生的路线中存在与新产生的路线相同的路线，则丢弃所述新产生的路线，并且利用遗传算法重新产生路线，直到不存在重复路线为止。
在步骤S104，将新的路线集合作为第一路线集合重复执行所述计算步骤和所述组合步骤，直到满足终止条件为止，并且将满足终止条件时与最高评价指标相对应的路线确定为优化的配送路线。
下面将参考图2结合具体的系统仿真软件来描述根据本公开的实施例的用于利用遗传算法对配送路线进行优化的方法。
在步骤S201，建立仿真模型库。可以使用物流系统仿真软件，例如flexsim、Automod等建立配送路线优化系统，如图3所示，包括仿真模型库、编写遗传算法所需要的树节点、模型控制界面的图形用户界面(GUI)。
当构建仿真模型库时，首先绘制所需的三维图，例如分省份绘制中国地图，然后导入到仿真软件中；接着建立仿真模型所需的实体，比如产生配送车辆的实体、配送站点的实体，并将这些实体保存成部件库。
在部件库中添加遗传算法节点，如图3所示，图中文件夹型的节点用于控制遗传算法的相关参数，S型的节点用于编写遗传算法。
模型控制界面GUI是仿真软件的一个功能，只需将现有的控件拖入GUI界面中进行布局，然后与模型库、遗传算法参数节点进行链接。
在步骤S202，设置站点。在GUI界面的布局选项卡中输入配送站点的数量，例如，设置10个配送站点，输出配送站点表，并在表中的每一行输入一个配送站点所在的城市名称。
在步骤S203，设置站点之间的距离。在GUI界面的布局选项卡中，点击设置距离表格，在弹出的表格中输入两两站点之间的距离。
在步骤S204，生成具体的配送路线模型。通过步骤S202和S203设置的参数，使用仿真模型库中的实体，生成具体的仿真模型，如图4所示，图中一个红色的五角星就是一个配送站点，任意两个配送站点之间都有一条，如图4中连接两点的线，车辆可以从某一点出发，按照假定的经过所有配送站点一次后，输出配送所花费的时间作为评价本次假定配送好坏的指标。
在步骤S205，设置遗传算法的相关参数。在GUI界面的遗传算法选项卡中输入参数，包括路线集合大小、迭代次数、选择、交叉、变异。这里的路线集合就是配送路线优化问题的一个可行解集，解集里解的个数即配送的个数就是路线集合大小，一个解或者一条配送路线称作一个个体。迭代次数作为遗传算法终止的条件。选择、交叉、变异是遗传算法中的基本 算子，用于在迭代过程中产生新的路线集合。选择是从当前路线集合中挑出优胜个体并保留到新路线集合的操作，交叉和变异是将当前路线集合中的个体进行重组，从而得到新个体的操作，挑出的优胜个体和重组得到的新个体就组成了一个新的路线集合。
在步骤S206，初始化路线集合。使用遗传算法树节点中的Initialize population子节点，随机产生路线集合。该过程如下：对输入的站点进行编号。例如，如前所述，在步骤S202中输入了10个站点，则用1到10之间的整数对站点进行编号，将站点编号随机打乱得到一个数字序列，这个序列就代表一条配送，也可以称作一个个体，随机产生路线集合大小个个体，就组成了初始路线集合。
在步骤S207，运行在步骤S204中生成的配送路线模型，得出评价指标。把个体作为仿真模型的输入，运行仿真模型，并把得出的配送时间作为评价个体好坏的指标。
在步骤S208，判断当前路线集合是否运行完成，如果没有完成，则继续执行步骤S207，如果完成，则执行步骤S209。
在步骤S209，判断是否满足终止条件。这里的终止条件是迭代次数达到设定的目标值，如果不满足终止条件，则执行步骤S210，如果满足终止条件，则执行步骤S211。
在步骤S210，运行新的路线集合模型得出评价指标并跳往步骤S207。具体地，使用遗传算法树节点中的generate_next_population子节点生成新的路线集合，该过程包括：通过选择算子保留一定量的优胜个体，再通过交叉、变异算子重组得到一些新的个体，从而组成新的路线集合。
在步骤S211，停止运行配送路线模型得出配送路线。该配送路线是当前路线集合中评价指标最高的路线。
在整个优化过程中，仿真模型运行的次数等于路线集合大小乘以迭代次数，最后一次运行的路线就是优化后的配送路线。
应当注意的是，可以选择不同的物流系统仿真软件和智能优化算法来对配送路线进行优化。
本公开的技术方案避免了用静态的思维求解动态的问题，并且其操作简单，只需通过输入配送站点数量、站点所在的城市名称以及两两站点之间的距离，就可以通过仿真实验确定出一条优化配送。
图5示意性地示出了根据本公开的实施例的用于利用遗传算法对配送路线进行优化的系统500的框图。系统500包括处理器510，例如，数字信号处理器(DSP)。处理器510可以是用于执行本文所描述的方法100的不同动作的单个装置或多个装置。具体地，处理器510被配置为执行指令以：根据与站点相对应的地理位置来生成第一路线集合，其中，所述第一 路线集合包括N条路线；计算与所述第一路线集合中的各条路线相对应的评价指标；确定是否满足终止条件，当不满足所述终止条件时，利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行处理以产生N-m条路线，并且将所述N-m条路线与所述评价指标排名最高的m条路线组合为新的路线集合，其中m＜N以及将所述新的路线集合作为第一路线集合重复执行所述计算步骤和所述组合步骤，直到满足终止条件为止，并且将满足终止条件时与最高评价指标相对应的路线确定为优化的配送路线。所述处理器510可以被进一步配置为执行指令以：当利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行交叉时，从所述m条路线中任意选择两条路线并对所述两条路线进行交叉以产生一条新路线，以及当利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行变异时，从所述m条路线中任意选择一条路线并对所述一条路线进行变异以产生一条新路线。
系统500还可以包括输入/输出(I/O)装置530，用于从其他实体接收信号或者向其他实体发送信号。
此外，系统500包括存储器520，该存储器520可以具有以下形式：非易失性或易失性存储器，例如，电可擦除可编程只读存储器(EEPROM)、闪存等。存储器520存储计算机可读指令，当处理器510执行该计算机可读指令时，该计算机可读指令使处理器执行本文所述的动作。
附图中示出了一些方框图和/或流程图。应理解，方框图和/或流程图中的一些方框或其组合可以由计算机程序指令来实现。这些计算机程序指令可以提供给通用计算机、专用计算机或其他可编程数据处理装置的处理器，从而这些指令在由该处理器执行时可以创建用于实现这些方框图和/或流程图中所说明的功能/操作的装置。
因此，本公开的技术可以硬件和/或软件(包括固件、微代码等)的形式来实现。另外，本公开的技术可以采取存储有指令的计算机可读介质上的计算机程序产品的形式，该计算机程序产品可供指令执行系统(例如，一个或多个处理器)使用或者结合指令执行系统使用。在本公开的上下文中，计算机可读介质可以是能够包含、存储、传送、传播或传输指令的任意介质。例如，计算机可读介质可以包括但不限于电、磁、光、电磁、红外或半导体系统、装置、器件或传播介质。计算机可读介质的具体示例包括：磁存储装置，如磁带或硬盘(HDD)；光存储装置，如光盘(CD-ROM)；存储器，如随机存取存储器(RAM)或闪存；和/或有线/无线通信链路。
以上的详细描述通过使用示意图、流程图和/或示例，已经阐述了用于利用遗传算法对配送路线进行优化的方法和利用遗传算法对配送路线进行优化的系统的众多实施例。在这种示 意图、流程图和/或示例包含一个或多个功能和/或操作的情况下，本领域技术人员应理解，这种示意图、流程图或示例中的每一功能和/或操作可以通过各种结构、硬件、软件、固件或实质上它们的任意组合来单独和/或共同实现。在一个实施例中，本公开的实施例所述主题的若干部分可以通过专用集成电路(ASIC)、现场可编程门阵列(FPGA)、数字信号处理器(DSP)、或其他集成格式来实现。然而，本领域技术人员应认识到，这里所公开的实施例的一些方面在整体上或部分地可以等同地实现在集成电路中，实现为在一台或多台计算机上运行的一个或多个计算机程序(例如，实现为在一台或多台计算机系统上运行的一个或多个程序)，实现为在一个或多个处理器上运行的一个或多个程序(例如，实现为在一个或多个微处理器上运行的一个或多个程序)，实现为固件，或者实质上实现为上述方式的任意组合，并且本领域技术人员根据本公开，将具备设计电路和/或写入软件和/或固件代码的能力。此外，本领域技术人员将认识到，本公开所述主题的机制能够作为多种形式的程序产品进行分发，并且无论实际用来执行分发的信号承载介质的具体类型如何，本公开所述主题的示例性实施例均适用。信号承载介质的示例包括但不限于：可记录型介质，如软盘、硬盘驱动器、紧致盘(CD)、数字通用盘(DVD)、数字磁带、计算机存储器等；以及传输型介质，如数字和/或模拟通信介质(例如，光纤光缆、波导、有线通信链路、无线通信链路等)。

Claims (21)

  1. 一种用于利用遗传算法对配送路线进行优化的方法，包括：
    根据与站点相对应的地理位置来生成第一路线集合，其中，所述第一路线集合包括N条路线；
    计算与所述第一路线集合中的各条路线相对应的评价指标；
    确定是否满足终止条件，当不满足所述终止条件时，利用遗传算法对从所述第一路线集合中选择出的评价指标排名最高的m条路线进行处理以产生N-m条路线，并且将所述N-m条路线与所述评价指标排名最高的m条路线组合为新的路线集合，其中m＜N；以及
    将所述新的路线集合作为第一路线集合重复执行所述计算步骤和所述组合步骤，直到满足终止条件为止，并且将满足终止条件时与最高评价指标相对应的路线确定为优化的配送路线。
  2. 根据权利要求1所述的方法，其中，所述第一路线集合是随机产生的。
  3. 根据权利要求1所述的方法，其中，所述评价指标是按照相应路线进行配送所花费的时间。
  4. 根据权利要求1所述的方法，其中，所述终止条件是达到预先设置的迭代次数。
  5. 根据权利要求1所述的方法，其中，所产生的N-m条路线中的每一条路线是通过利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行交叉或变异获得的。
  6. 根据权利要求5所述的方法，其中，
    当利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行交叉时，所述方法包括：从所述m条路线中任意选择两条路线并对所述两条路线进行交叉以产生一条新路线，以及
    当利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行变异时，所述方法包括：从所述m条路线中任意选择一条路线并对所述一条路线进行变异以产生一条新路线。
  7. 根据权利要求1所述的方法，其中，所述新的路线集合中的路线各不相同。
  8. 一种用于利用遗传算法对配送路线进行优化的系统，包括：
    处理器，被配置为执行指令以：
    根据与站点相对应的地理位置来生成第一路线集合，其中，所述第一路线集合包括N条路线；
    计算与所述第一路线集合中的各条路线相对应的评价指标；
    确定是否满足终止条件，当不满足所述终止条件时，利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行处理以产生N-m条路线，并且将所述N-m条路线与所述评价指标排名最高的m条路线组合为新的路线集合，其中m＜N；以及
    将所述新的路线集合作为第一路线集合重复执行所述计算步骤和所述组合步骤，直到满足终止条件为止，并且将满足终止条件时与最高评价指标相对应的路线确定为优化的配送路线；以及
    存储器，被配置为存储所述指令。
  9. 根据权利要求8所述的系统，其中，所述第一路线集合是随机产生的。
  10. 根据权利要求8所述的系统，其中，所述评价指标是按照相应路线进行配送所花费的时间。
  11. 根据权利要求8所述的系统，其中，所述终止条件是达到预先设置的迭代次数。
  12. 根据权利要求8所述的系统，其中，所产生的N-m条路线中的每一条路线是通过利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行交叉或变异获得的。
  13. 根据权利要求12所述的系统，其中，所述处理器被进一步配置为执行指令以：
    当利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行交叉时，从所述m条路线中任意选择两条路线并对所述两条路线进行交叉以产生一条新路线，以及
    当利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行变异时，从所述m条路线中任意选择一条路线并对所述一条路线进行变异以产生一条新路线。
  14. 根据权利要求8所述的系统，其中，所述新的路线集合中的路线各不相同。
  15. 一种计算机可读介质，包括在由处理器执行时执行包括以下各项的操作的指令：
    根据与站点相对应的地理位置来生成第一路线集合，其中，所述第一路线集合包括N条路线；
    计算与所述第一路线集合中的各条路线相对应的评价指标；
    确定是否满足终止条件，当不满足所述终止条件时，利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行处理以产生N-m条路线，并且将所述N-m条路线与所述评价指标排名最高的m条路线组合为新的路线集合，其中m＜N；以及
    将所述新的路线集合作为第一路线集合重复执行所述计算步骤和所述组合步骤，直到满足终止条件为止，并且将满足终止条件时与最高评价指标相对应的路线确定为优化的配送路 线。
  16. 根据权利要求15所述的计算机可读介质，其中，所述第一路线集合是随机产生的。
  17. 根据权利要求15所述的计算机可读介质，其中，所述评价指标是按照相应路线进行配送所花费的时间。
  18. 根据权利要求15所述的计算机可读介质，其中，所述终止条件是达到预先设置的迭代次数。
  19. 根据权利要求15所述的计算机可读介质，其中，所产生的N-m条路线中的每一条路线是通过利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行交叉或变异获得的。
  20. 根据权利要求19所述的计算机可读介质，其中，所述处理器被进一步配置为执行指令以：
    当利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行交叉时，从所述m条路线中任意选择两条路线并对所述两条路线进行交叉以产生一条新路线，以及
    当利用遗传算法对从所述第一路线集合中选择的评价指标排名最高的m条路线进行变异时，从所述m条路线中任意选择一条路线并对所述一条路线进行变异以产生一条新路线。
  21. 根据权利要求15所述的计算机可读介质，其中，所述新的路线集合中的路线各不相同。
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109886490A (zh) * 2019-02-22 2019-06-14 广西大学 一种组合车辆联合运输的匹配优化方法
CN109992899A (zh) * 2019-04-08 2019-07-09 中国人民解放军陆军防化学院 一种基于改进的slp和遗传算法的洗消站装备设施布局方法
CN111489194A (zh) * 2020-04-01 2020-08-04 拉扎斯网络科技（上海）有限公司 地图信息处理方法、装置、可读存储介质和电子设备
CN112149921A (zh) * 2020-10-20 2020-12-29 国网重庆市电力公司营销服务中心 大规模电动物流车路径规划方法及系统、充电规划方法
CN114186479A (zh) * 2021-11-16 2022-03-15 北京百度网讯科技有限公司 冲压工艺参数处理方法、装置、电子设备及存储介质
CN116011787A (zh) * 2023-02-09 2023-04-25 北京云思智学科技有限公司 智能排课方法、装置、电子设备及计算机可读存储介质

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106845665A (zh) * 2016-11-17 2017-06-13 北京京东尚科信息技术有限公司 用于利用遗传算法对配送路线进行优化的方法和系统
CN108647810A (zh) * 2018-04-19 2018-10-12 安吉汽车物流股份有限公司 订单运输的分配方法及装置、计算机可读介质
CN114024848A (zh) * 2020-11-23 2022-02-08 北京八分量信息科技有限公司 一种通过优化节点通讯来提高系统健壮性的方法
CN116187505B (zh) * 2022-12-01 2025-11-14 国家能源投资集团有限责任公司 供应链视角下煤炭调运路线优化方法及系统

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103440522A (zh) * 2013-08-31 2013-12-11 福州大学 遗传算法与MapReduce相结合的车辆调度方法
CN103761588A (zh) * 2014-02-18 2014-04-30 张家港美核电子科技有限公司 基于多目标建模优化的危化品运输调度方法
CN105260787A (zh) * 2015-09-15 2016-01-20 浪潮软件股份有限公司 一种基于遗传算法的分拣作业均衡调度方法
CN105868843A (zh) * 2016-03-22 2016-08-17 南京邮电大学 一种面向货品配送的路线规划方法
CN106845665A (zh) * 2016-11-17 2017-06-13 北京京东尚科信息技术有限公司 用于利用遗传算法对配送路线进行优化的方法和系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103440522A (zh) * 2013-08-31 2013-12-11 福州大学 遗传算法与MapReduce相结合的车辆调度方法
CN103761588A (zh) * 2014-02-18 2014-04-30 张家港美核电子科技有限公司 基于多目标建模优化的危化品运输调度方法
CN105260787A (zh) * 2015-09-15 2016-01-20 浪潮软件股份有限公司 一种基于遗传算法的分拣作业均衡调度方法
CN105868843A (zh) * 2016-03-22 2016-08-17 南京邮电大学 一种面向货品配送的路线规划方法
CN106845665A (zh) * 2016-11-17 2017-06-13 北京京东尚科信息技术有限公司 用于利用遗传算法对配送路线进行优化的方法和系统

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109886490A (zh) * 2019-02-22 2019-06-14 广西大学 一种组合车辆联合运输的匹配优化方法
CN109992899A (zh) * 2019-04-08 2019-07-09 中国人民解放军陆军防化学院 一种基于改进的slp和遗传算法的洗消站装备设施布局方法
CN109992899B (zh) * 2019-04-08 2023-04-14 中国人民解放军陆军防化学院 一种基于改进的slp和遗传算法的洗消站装备设施布局方法
CN111489194A (zh) * 2020-04-01 2020-08-04 拉扎斯网络科技（上海）有限公司 地图信息处理方法、装置、可读存储介质和电子设备
CN111489194B (zh) * 2020-04-01 2023-12-08 拉扎斯网络科技（上海）有限公司 地图信息处理方法、装置、可读存储介质和电子设备
CN112149921A (zh) * 2020-10-20 2020-12-29 国网重庆市电力公司营销服务中心 大规模电动物流车路径规划方法及系统、充电规划方法
CN112149921B (zh) * 2020-10-20 2024-04-19 国网重庆市电力公司营销服务中心 大规模电动物流车路径规划方法及系统、充电规划方法
CN114186479A (zh) * 2021-11-16 2022-03-15 北京百度网讯科技有限公司 冲压工艺参数处理方法、装置、电子设备及存储介质
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