A method for allocating and scheduling automobile logistics routes

Abstract

The present invention discloses a method for allocating and scheduling automobile logistics routes, which belongs to the technical field of logistics route planning. By providing a new coding method to convert the route planning problem into a continuously solvable problem, the solution efficiency of automobile logistics route allocation and scheduling can be effectively improved. A logistics route allocation and scheduling optimization algorithm is provided, which can quickly solve the vector obtained by coding, so as to determine the optimal scheduling route. Compared with the prior art, the method has higher automobile logistics route allocation and scheduling efficiency, and can effectively save transportation costs and transportation time.

Description

Technical Field

[0001] The invention belongs to the technical field of logistics route planning, and in particular relates to a method for allocating and scheduling automobile logistics routes. Background Art

[0002] Logistics refers to the effective flow and storage process of goods from the place of production to the place of consumption. It involves planning, implementing and controlling the effective flow and storage of products to meet the needs of consumers. Logistics is a key component of supply chain management, and its goal is to achieve the best results of goods, services and related information at the right time and place at the lowest cost. With the rapid development of the economy, the automobile logistics industry is facing huge challenges. Traditional logistics route allocation and scheduling methods often rely on manual experience, and have problems such as low efficiency and high cost. Therefore, it is of great practical significance to study an efficient automobile logistics route allocation and scheduling method. Summary of the invention

[0003] The present invention provides an automobile logistics route allocation and scheduling method, which is used to solve the problems of low efficiency and high cost caused by the prior art relying on manual experience for logistics planning.

[0004] A method for allocating and scheduling automobile logistics routes, comprising:

[0005] Determine multiple target logistics nodes, and for each target logistics node, randomly generate a value within a value range;

[0006] The values ​​corresponding to all target logistics nodes are combined into a vector to obtain the value vector corresponding to the target logistics node, and multiple different value vectors are obtained;

[0007] The logistics route allocation scheduling optimization algorithm is used to optimize multiple different value vectors to determine the optimal value vector;

[0008] The optimal value vector is decoded to obtain scheduling routes corresponding to multiple target logistics nodes, and automobile logistics is scheduled according to the scheduling routes.

[0009] Furthermore, multiple target logistics nodes are determined, and for each target logistics node, a value is randomly generated within a value range, including:

[0010] Obtain multiple target logistics nodes input by staff through human-computer interaction;

[0011] The value interval is determined to be (0,1), and a value is randomly generated for each target logistics node between (0,1).

[0012] Furthermore, the values ​​corresponding to all target logistics nodes are combined into a vector to obtain the value vector corresponding to the target logistics node, and multiple different value vectors are obtained, including:

[0013] For all target logistics nodes, the position of the element corresponding to the target logistics node in the vector is fixed, and the value corresponding to the target logistics node is placed in the corresponding position to obtain the value vector corresponding to the target logistics node, and obtain multiple different value vectors.

[0014] Furthermore, a logistics route allocation scheduling optimization algorithm is used to optimize multiple different value vectors to determine the optimal value vector, including:

[0015] Set the optimization counter t=1 and the maximum number of optimizations T;

[0016] A fitness function is constructed using the transportation time and the transportation cost, a fitness value corresponding to each value vector is obtained according to the fitness function, and an optimal value vector is obtained according to the fitness value corresponding to the value vector;

[0017] According to the optimal value vector, a multi-space search strategy is used to perform an extended search on the value vector to expand the search range and obtain a value vector after the extended search;

[0018] For the value vector after the extended search, a chaotic mapping search strategy is used to perform a spatial ergodic search on the value vector to obtain the value vector after the spatial ergodic search;

[0019] For the value vector after the spatial ergodic search, a random flight search strategy is used to perform a random flight search on the value vector to obtain the value vector after the random flight search;

[0020] For the value vector after random flight search, an adaptive weight search strategy is used to perform adaptive fine search on the value vector to obtain the value vector after adaptive fine search;

[0021] Determine whether the count value of the optimization counter t is equal to or greater than the maximum optimization times T. If so, re-obtain the optimal value vector based on the value vector after the adaptive fine search and output it. Otherwise, return to the step of performing the extended search.

[0022] Further, a fitness function is constructed using the transportation time and the transportation cost, a fitness value corresponding to each value vector is obtained according to the fitness function, and an optimal value vector is obtained according to the fitness value corresponding to the value vector, including:

[0023] The transport time corresponding to the value vector is taken inversely to obtain the first inverse term; the transport cost corresponding to the value vector is taken inversely to obtain the second inverse term, and the first inverse term and the second inverse term are added to obtain the fitness function; wherein, the larger the fitness function is, the better the value vector is;

[0024] For any value vector, the target logistics nodes in the value vector are arranged in ascending order of value to obtain the target logistics node sequence;

[0025] When there is no need to return to the vehicle exit node, the vehicle exit node is added to the front of the target logistics node sequence to obtain the vehicle logistics path;

[0026] When it is necessary to return to the vehicle exit node, the vehicle exit node is added to the front and back of the target logistics node sequence to obtain the vehicle logistics path;

[0027] Obtain the target transportation time and target transportation cost corresponding to the automobile logistics path, and determine the fitness value corresponding to the value vector according to the fitness function, the target transportation time and the target transportation cost;

[0028] Obtain the fitness values ​​corresponding to all value vectors, and obtain the optimal value vector according to the fitness values ​​corresponding to all value vectors.

[0029] Further, according to the optimal value vector, a multi-space search strategy is used to perform an extended search on the value vector to expand the search range and obtain a value vector after the extended search, including:

[0030] For any value vector, determine the distance in each dimension between the value vector and the optimal value vector: ;in, Indicates t During the optimization process i The value vector d The dimension value and the optimal value vector d The first search distance between dimension values; Indicates t The optimal value vector in the optimization process d Dimension value, Indicates t During the optimization process i The value vector d Dimension value, i =1,2,...,I, I represents the total number of value vectors, d =1,2,...,D, where D represents the total number of target logistics nodes;

[0031] The first search distance Based on, the second search distance and the third search distance are determined as: = * , = * ;in, represents the second search distance, represents the third search distance, represents the first random number between (0,1), Represents the second random number between (0,1);

[0032] The random first search angle and the second search angle are determined as: , ;in, represents the first search angle, represents the second search angle, represents the third random number between (0,1), represents the fourth random number between (0,1), represents pi;

[0033] According to the first search distance , Second search distance , the third search distance , First search angle And the second search angle , determine the first spatial search factor and the second spatial search factor as: , ;in, represents the first spatial search factor, represents the second spatial search factor;

[0034] According to the first spatial search factor and the second space search factor , perform an extended search on the value vector, and the value vector after the extended search is: ;in, Indicates i The value vector after the extended search d Dimension value, represents the fifth random number between (0,1), Represents the sixth random number between (0,1).

[0035] Furthermore, for the value vector after the extended search, a chaotic mapping search strategy is used to perform a spatial ergodic search on the value vector, and the value vector after the spatial ergodic search is obtained, including:

[0036] Determine the chaotic mapping factor as: ;in, Indicates t +1 chaotic mapping factor during the optimization process, Indicates t The chaotic mapping factor in the sub-optimization process, mod represents the remainder function, represents the first constant term, and b =0.2; c represents the second constant term, and c =0.5; represents pi;

[0037] According to the current optimization times, the nonlinear chaotic search control factor is determined as: ;in, r represents the nonlinear chaos search control factor, q represents the third constant term, and q =2; e represents a natural constant, T represents the maximum number of optimization times;

[0038] For the value vector after the extended search, according to the chaotic mapping factor and nonlinear chaos search control factor r , perform a spatial ergodic search on the value vector, and the value vector after the spatial ergodic search is: ;in, Indicates t During the optimization process k The value vector after the extended search d Dimension value, k =1,2,...,I, I represents the total number of value vectors, Indicates k The value vector after the space traversal search d Dimension value, Indicates t The optimal value vector in the optimization process d dimension value, cos represents the cosine function, means (0,2 ) at random angles between .

[0039] Furthermore, for the value vector after the spatial ergodic search, a random flight search strategy is used to perform a random flight search on the value vector, and the value vector after the random flight search is obtained, including:

[0040] For any value vector after space traversal search, a corresponding random flight vector is generated using the Levy flight strategy;

[0041] According to the random flight vector, a random flight search is performed on the value vector, and the value vector after the random flight search is obtained as follows: +| |* ;in, Indicates t During the optimization process m The value vector after the space traversal search d Dimension value, Indicates m The value vector after the random flight search d Dimension value, m =1,2,...,I, I represents the total number of value vectors, represents the random flight vector d Dimension value, represents the seventh random number between (0,1), cos represents the cosine function, Represents pi.

[0042] Furthermore, for the value vector after the random flight search, an adaptive weight search strategy is used to perform an adaptive fine search on the value vector to obtain the value vector after the adaptive fine search, including:

[0043] According to the current optimization times, the adaptive weight is determined as: ;in, Indicates t The adaptive weight in the sub-optimization process, sin represents the sine function, represents pi, Indicates the maximum number of optimizations;

[0044] According to the adaptive weight, an adaptive fine search is performed on the value vector, and the value vector obtained after the adaptive fine search is: ;in, Indicates t During the optimization process n The value vector after the random flight search d Dimension value, Indicates n The value vector after the adaptive fine search d Dimension value, represents the nonlinear chaos search control factor, Indicates t The optimal value vector in the optimization process d Dimension value, n =1,2,...,I, I represents the total number of value vectors, represents the eighth random number between (0,1), Represents the ninth random number between (0,1).

[0045] Furthermore, the optimal value vector is decoded to obtain scheduling routes corresponding to multiple target logistics nodes, and automobile logistics is scheduled according to the scheduling routes, including:

[0046] Arrange the target logistics nodes in the optimal value vector in ascending order of value to obtain the optimal target logistics node sequence;

[0047] When there is no need to return to the vehicle exit node, the vehicle exit node is added to the front of the optimal target logistics node sequence to obtain the scheduling routes corresponding to multiple target logistics nodes;

[0048] When it is necessary to return to the vehicle exit node, the vehicle exit node is added to the front and back of the optimal target logistics node sequence to obtain the scheduling routes corresponding to multiple target logistics nodes;

[0049] The automobile logistics is dispatched according to the dispatching route.

[0050] The present invention provides a method for allocating and scheduling automobile logistics routes. By providing a new coding method to convert the route planning problem into a continuously solvable problem, the solution efficiency of the automobile logistics route allocation and scheduling can be effectively improved. A logistics route allocation and scheduling optimization algorithm is also provided, which can quickly solve the vector obtained by coding to determine the optimal scheduling route. Compared with the prior art, the method has higher efficiency in allocating and scheduling automobile logistics routes and can effectively save transportation costs and transportation time. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0052] Figure 1 A flowchart of a method for allocating and scheduling automobile logistics routes provided in an embodiment of the present invention.

[0053] Figure 2 A flowchart for determining an optimal value vector provided by an embodiment of the present invention.

[0054] The above drawings have shown clear embodiments of the present invention, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the present invention in any way, but to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0055] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0056] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0057] like Figure 1 As shown, an embodiment of the present invention provides a method for allocating and scheduling automobile logistics routes, including:

[0058] S101, determining multiple target logistics nodes, and for each target logistics node, randomly generating a value within a value range;

[0059] The target logistics node refers to the node (ie, the destination) to which the automobile logistics is to be delivered. In order to facilitate the subsequent solution of the automobile logistics route, the embodiment of the present invention first assigns a value to the target logistics node, thereby achieving efficient route planning.

[0060] S102, the values ​​corresponding to all target logistics nodes are combined into a vector to obtain a value vector corresponding to the target logistics node, and multiple different value vectors are obtained;

[0061] For example, when there are three first target logistics nodes A1, the second target logistics node A2 and the third target logistics node A3, a vector (A1, A2, A3) can be constructed, and then the values ​​of the first target logistics node A1, the second target logistics node A2 and the third target logistics node A3 are filled in the corresponding positions to obtain the value vector corresponding to the target logistics node.

[0062] S103, optimizing multiple different value vectors using a logistics route allocation scheduling optimization algorithm to determine an optimal value vector;

[0063] After encoding the route allocation and scheduling problem into value vectors, it is necessary to solve these value vectors to find the optimal value vector, so as to determine the scheduling route. In the prior art, genetic algorithms or ant colony algorithms are often used for route allocation and scheduling, but these algorithms often have the problem of being prone to local optimality and low efficiency, resulting in the final determined route not being the optimal route. Therefore, an embodiment of the present invention provides a logistics route allocation and scheduling optimization algorithm to solve the problem of being prone to local optimality and low efficiency in the prior art, and improve the ability to find the optimal route.

[0064] S104, decoding the optimal value vector to obtain scheduling routes corresponding to multiple target logistics nodes, and scheduling automobile logistics according to the scheduling routes.

[0065] The present invention provides a method for allocating and scheduling automobile logistics routes. By providing a new coding method to convert the route planning problem into a continuously solvable problem, the solution efficiency of the automobile logistics route allocation and scheduling can be effectively improved. A logistics route allocation and scheduling optimization algorithm is also provided, which can quickly solve the vector obtained by coding to determine the optimal scheduling route. Compared with the prior art, the method has higher efficiency in allocating and scheduling automobile logistics routes and can effectively save transportation costs and transportation time.

[0066] In the embodiment of the present invention, a plurality of target logistics nodes are determined, and for each target logistics node, a value is randomly generated within a value range, including:

[0067] Obtain multiple target logistics nodes input by staff through human-computer interaction;

[0068] The value interval is determined to be (0,1), and a value is randomly generated for each target logistics node between (0,1).

[0069] It is worth noting that the value interval is set to (0, 1) only to reduce the amount of calculation and is only an example of an embodiment of the present invention. The value interval can also be set to other intervals, which can also achieve the purpose of the embodiment of the present invention.

[0070] In the embodiment of the present invention, the values ​​corresponding to all target logistics nodes are combined into a vector to obtain a value vector corresponding to the target logistics node, and multiple different value vectors are obtained, including:

[0071] For all target logistics nodes, the position of the element corresponding to the target logistics node in the vector is fixed, and the value corresponding to the target logistics node is placed in the corresponding position to obtain the value vector corresponding to the target logistics node, and obtain multiple different value vectors.

[0072] like Figure 2 As shown in the figure, the logistics route allocation scheduling optimization algorithm is used to optimize multiple different value vectors to determine the optimal value vector, including:

[0073] S201, setting the optimization counter t=1 and the maximum number of optimization times T;

[0074] S202, constructing a fitness function using the transportation time and the transportation cost, obtaining a fitness value corresponding to each value vector according to the fitness function, and obtaining an optimal value vector according to the fitness value corresponding to the value vector;

[0075] S203, according to the optimal value vector, adopt a multi-space search strategy to perform an extended search on the value vector to expand the search range and obtain a value vector after the extended search;

[0076] S204, for the value vector after the extended search, a chaotic mapping search strategy is used to perform a spatial ergodic search on the value vector to obtain the value vector after the spatial ergodic search;

[0077] S205, for the value vector after the spatial ergodic search, a random flight search strategy is used to perform a random flight search on the value vector to obtain the value vector after the random flight search;

[0078] S206, for the value vector after the random flight search, use an adaptive weight search strategy to perform an adaptive fine search on the value vector to obtain the value vector after the adaptive fine search;

[0079] S207, determine whether the count value of the optimization counter t is equal to or greater than the maximum optimization times T, if so, re-obtain the optimal value vector according to the value vector after the adaptive fine search and output it, otherwise return to the step of performing the extended search.

[0080] The logistics route allocation and scheduling optimization algorithm provided by the embodiment of the present invention can not only realize fast search, but also avoid falling into local optimum, thereby assisting users to efficiently perform logistics route allocation and scheduling.

[0081] In the embodiment of the present invention, a fitness function is constructed using transportation time and transportation cost, a fitness value corresponding to each value vector is obtained according to the fitness function, and an optimal value vector is obtained according to the fitness value corresponding to the value vector, including:

[0082] The transport time corresponding to the value vector is taken inversely to obtain the first inverse term; the transport cost corresponding to the value vector is taken inversely to obtain the second inverse term, and the first inverse term and the second inverse term are added to obtain the fitness function; wherein, the larger the fitness function is, the better the value vector is;

[0083] Transportation time can be obtained using historical data, for example, the average travel time between any two target logistics nodes can be obtained, but the accuracy of this method is low and can only achieve a rough estimate. In order to further improve the accuracy of transportation time, historical travel data for the previous period can be obtained, and then when planning logistics routes, the data for the corresponding time period of the same type of date is called and the average transportation time is calculated. For example, when planning a transportation route at 15:30 on Tuesday, the average transportation time from 15:00 to 16:00 on Tuesday of the previous month is called.

[0084] However, it is worth mentioning that in addition to obtaining transportation time through historical data, you can also call third-party maps to query the estimated travel time between any two target logistics nodes in real time.

[0085] The transportation cost can be obtained by the following method: the driving cost of an empty vehicle can be determined, and then the additional transportation cost of transporting a unit weight of goods by the vehicle can be obtained. After arriving at any target logistics node, the goods to be transported to the next target logistics node can be determined according to the transportation plan, thereby determining the transportation cost between any two target logistics nodes.

[0086] Furthermore, the transportation cost can be divided into flat roads, uphill roads and downhill roads, and each road section is calculated separately, so as to have higher accuracy. The flat roads, uphill roads and downhill roads in the transportation route can be obtained by calling a third-party map.

[0087] In actual scenarios, neither the transportation time nor the transportation cost can be zero, so the reciprocal can be directly taken.

[0088] For any value vector, the target logistics nodes in the value vector are arranged in ascending order of value to obtain the target logistics node sequence;

[0089] When there is no need to return to the vehicle exit node, the vehicle exit node is added to the front of the target logistics node sequence to obtain the vehicle logistics path;

[0090] When it is necessary to return to the vehicle exit node, the vehicle exit node is added to the front and back of the target logistics node sequence to obtain the vehicle logistics path;

[0091] Obtain the target transportation time and target transportation cost corresponding to the automobile logistics path, and determine the fitness value corresponding to the value vector according to the fitness function, the target transportation time and the target transportation cost;

[0092] Obtain the fitness values ​​corresponding to all value vectors, and obtain the optimal value vector according to the fitness values ​​corresponding to all value vectors.

[0093] It is worth noting that the final destination node can also be set, the vehicle exit node is placed before the target logistics node sequence, and the final destination node is placed after the target logistics node sequence, so that route planning can be realized.

[0094] In an embodiment of the present invention, based on the optimal value vector, a multi-space search strategy is used to perform an extended search on the value vector to expand the search range and obtain a value vector after the extended search, including:

[0095] For any value vector, determine the distance in each dimension between the value vector and the optimal value vector: ;in, Indicates t During the optimization process i The value vector d The dimension value and the optimal value vector d The first search distance between dimension values; Indicates t The optimal value vector in the optimization process d Dimension value, Indicates t During the optimization process i The value vector d Dimension value, i =1,2,...,I, I represents the total number of value vectors, d =1,2,...,D, where D represents the total number of target logistics nodes;

[0096] The first search distance Based on, the second search distance and the third search distance are determined as: = * , = * ;in, represents the second search distance, represents the third search distance, represents the first random number between (0,1), Represents the second random number between (0,1);

[0097] The random first search angle and the second search angle are determined as: , ;in, represents the first search angle, represents the second search angle, represents the third random number between (0,1), represents the fourth random number between (0,1), represents pi;

[0098] According to the first search distance , Second search distance , the third search distance , First search angle And the second search angle , determine the first spatial search factor and the second spatial search factor as: , ;in, represents the first spatial search factor, represents the second spatial search factor;

[0099] According to the first spatial search factor and the second space search factor , perform an extended search on the value vector, and the value vector after the extended search is: ;in, Indicates i The value vector after the extended search d Dimension value, represents the fifth random number between (0,1), Represents the sixth random number between (0,1).

[0100] The multi-space search strategy provided by the embodiment of the present invention can effectively improve the search range of the algorithm in the search space and improve the accuracy of the search direction by performing path search in the solution space to find a new position.

[0101] In the embodiment of the present invention, for the value vector after the extended search, a chaotic mapping search strategy is used to perform a spatial ergodic search on the value vector to obtain the value vector after the spatial ergodic search, including:

[0102] Determine the chaotic mapping factor as: ;in, Indicates t +1 chaotic mapping factor during the optimization process, Indicates t The chaotic mapping factor in the sub-optimization process, mod represents the remainder function, represents the first constant term, and b =0.2; c represents the second constant term, and c =0.5; represents pi;

[0103] According to the current optimization times, the nonlinear chaotic search control factor is determined as: ;in, r represents the nonlinear chaos search control factor, q represents the third constant term, and q =2; e represents a natural constant, T represents the maximum number of optimization times;

[0104] For the value vector after the extended search, according to the chaotic mapping factor and nonlinear chaos search control factor r, perform a spatial ergodic search on the value vector, and the value vector after the spatial ergodic search is: ;in, Indicates t During the optimization process k The value vector after the extended search d Dimension value, k =1,2,...,I, I represents the total number of value vectors, Indicates k The value vector after the space traversal search d Dimension value, Indicates t The optimal value vector in the optimization process d dimension value, cos represents the cosine function, means (0,2 ) at random angles between .

[0105] The chaotic mapping search strategy provided by the embodiment of the present invention introduces the idea of ​​chaotic mapping to perform search, so that the search positions are more evenly distributed in the solution space, the convenience of the solution space is increased, the convergence and convergence accuracy of the algorithm are improved, and the optimization time is shortened.

[0106] In the embodiment of the present invention, for the value vector after the spatial ergodic search, a random flight search strategy is used to perform a random flight search on the value vector to obtain the value vector after the random flight search, including:

[0107] For any value vector after space traversal search, a corresponding random flight vector is generated using the Levy flight strategy;

[0108] According to the random flight vector, a random flight search is performed on the value vector, and the value vector after the random flight search is obtained as follows: +| |* ;in, Indicates t During the optimization process m The value vector after the space traversal search d Dimension value, Indicates m The value vector after the random flight search d Dimension value, m =1,2,...,I, I represents the total number of value vectors, represents the random flight vector d Dimension value, represents the seventh random number between (0,1), cos represents the cosine function, Represents pi.

[0109] The random flight search strategy provided in the embodiment of the present invention utilizes the long-distance jump characteristics of Levy flight to perturb the position of the entire population, and combined with random directions, can effectively improve the global search capability of the algorithm and help to escape from the local optimal solution.

[0110] In the embodiment of the present invention, for the value vector after the random flight search, an adaptive weight search strategy is used to perform an adaptive fine search on the value vector to obtain the value vector after the adaptive fine search, including:

[0111] According to the current optimization times, the adaptive weight is determined as: ;in, Indicates t The adaptive weight in the sub-optimization process, sin represents the sine function, represents pi, Indicates the maximum number of optimizations;

[0112] According to the adaptive weight, an adaptive fine search is performed on the value vector, and the value vector obtained after the adaptive fine search is: ;in, Indicates t During the optimization process n The value vector after the random flight search d Dimension value, Indicates n The value vector after the adaptive fine search d Dimension value, represents the nonlinear chaos search control factor, Indicates t The optimal value vector in the optimization process d Dimension value, n =1,2,...,I, I represents the total number of value vectors, represents the eighth random number between (0,1), Represents the ninth random number between (0,1).

[0113] The adaptive weight search strategy provided by the embodiment of the present invention combines the adaptive weight control before and after the search, so that the algorithm has a faster search speed in the early stage and a higher search accuracy in the later stage of the algorithm, and searches near the optimal value vector, thereby achieving high-precision search.

[0114] In an embodiment of the present invention, decoding the optimal value vector to obtain scheduling routes corresponding to multiple target logistics nodes, and scheduling automobile logistics using the scheduling routes includes:

[0115] Arrange the target logistics nodes in the optimal value vector in ascending order of value to obtain the optimal target logistics node sequence;

[0116] When there is no need to return to the vehicle exit node, the vehicle exit node is added to the front of the optimal target logistics node sequence to obtain the scheduling routes corresponding to multiple target logistics nodes;

[0117] When it is necessary to return to the vehicle exit node, the vehicle exit node is added to the front and back of the optimal target logistics node sequence to obtain the scheduling routes corresponding to multiple target logistics nodes;

[0118] The automobile logistics is dispatched according to the dispatching route.

[0119] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0121] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0123] A person of ordinary skill in the art can understand that all or part of the steps in realizing the above-mentioned facts and methods can be completed by instructing the relevant hardware through a program, and the program involved or the program described can be stored in a computer-readable storage medium. When the program is executed, it includes the following steps: At this time, the corresponding method steps are derived, and the storage medium can be ROM / RAM, a disk, an optical disk, etc.

[0124] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for allocating and scheduling automobile logistics routes, characterized in that: include: Determine multiple target logistics nodes, and for each target logistics node, randomly generate a value within a value range; The values ​​corresponding to all target logistics nodes are combined into a vector to obtain the value vector corresponding to the target logistics node, and multiple different value vectors are obtained; The logistics route allocation scheduling optimization algorithm is used to optimize multiple different value vectors to determine the optimal value vector; Decoding the optimal value vector to obtain scheduling routes corresponding to multiple target logistics nodes, and scheduling automobile logistics according to the scheduling routes; Determine multiple target logistics nodes, and for each target logistics node, randomly generate a value within a value range, including: Obtain multiple target logistics nodes input by staff through human-computer interaction; Determine the value interval as (0,1), and randomly generate a value for each target logistics node between (0,1); The values ​​corresponding to all target logistics nodes are combined into a vector to obtain the value vector corresponding to the target logistics node, and multiple different value vectors are obtained, including: For all target logistics nodes, the position of the element corresponding to the target logistics node in the vector is fixed, and the value corresponding to the target logistics node is placed in the corresponding position to obtain the value vector corresponding to the target logistics node, and obtain multiple different value vectors; The logistics route allocation scheduling optimization algorithm is used to optimize multiple different value vectors to determine the optimal value vector, including: Set the optimization counter t=1 and the maximum number of optimizations T; A fitness function is constructed using the transportation time and the transportation cost, a fitness value corresponding to each value vector is obtained according to the fitness function, and an optimal value vector is obtained according to the fitness value corresponding to the value vector; According to the optimal value vector, a multi-space search strategy is used to perform an extended search on the value vector to expand the search range and obtain a value vector after the extended search; For the value vector after the extended search, a chaotic mapping search strategy is used to perform a spatial ergodic search on the value vector to obtain the value vector after the spatial ergodic search; For the value vector after the spatial ergodic search, a random flight search strategy is used to perform a random flight search on the value vector to obtain the value vector after the random flight search; For the value vector after random flight search, an adaptive weight search strategy is used to perform adaptive fine search on the value vector to obtain the value vector after adaptive fine search; Determine whether the count value of the optimization counter t is equal to or greater than the maximum optimization times T. If so, re-obtain the optimal value vector according to the value vector after the adaptive fine search and output it. Otherwise, return to the step of performing the extended search. A fitness function is constructed using transportation time and transportation cost, a fitness value corresponding to each value vector is obtained according to the fitness function, and an optimal value vector is obtained according to the fitness value corresponding to the value vector, including: The transport time corresponding to the value vector is taken inversely to obtain the first inverse term; the transport cost corresponding to the value vector is taken inversely to obtain the second inverse term, and the first inverse term and the second inverse term are added to obtain the fitness function; wherein, the larger the fitness function is, the better the value vector is; For any value vector, the target logistics nodes in the value vector are arranged in ascending order of value to obtain the target logistics node sequence; When there is no need to return to the vehicle exit node, the vehicle exit node is added to the front of the target logistics node sequence to obtain the vehicle logistics path; When it is necessary to return to the vehicle exit node, the vehicle exit node is added to the front and back of the target logistics node sequence to obtain the vehicle logistics path; Obtain the target transportation time and target transportation cost corresponding to the automobile logistics path, and determine the fitness value corresponding to the value vector according to the fitness function, the target transportation time and the target transportation cost; Obtain the fitness values ​​corresponding to all value vectors, and obtain the optimal value vector according to the fitness values ​​corresponding to all value vectors; According to the optimal value vector, a multi-space search strategy is used to perform an extended search on the value vector to expand the search range, and a value vector after the extended search is obtained, including: For any value vector, determine the distance in each dimension between the value vector and the optimal value vector: ;in, Indicates t During the optimization process i The value vector d The dimension value and the optimal value vector d The first search distance between dimension values; Indicates t The optimal value vector in the optimization process d Dimension value, Indicates t During the optimization process i The value vector d Dimension value, i =1,2,...,I, I represents the total number of value vectors, d =1,2,...,D, where D represents the total number of target logistics nodes; The first search distance Based on, the second search distance and the third search distance are determined as: = * , = * ;in, represents the second search distance, represents the third search distance, represents the first random number between (0,1), Represents the second random number between (0,1); The random first search angle and the second search angle are determined as: , ;in, represents the first search angle, represents the second search angle, represents the third random number between (0,1), represents the fourth random number between (0,1), represents pi; According to the first search distance , Second search distance , the third search distance , First search angle And the second search angle , determine the first spatial search factor and the second spatial search factor as: , ;in, represents the first spatial search factor, represents the second spatial search factor; According to the first spatial search factor and the second spatial search factor , perform an extended search on the value vector, and the value vector after the extended search is: ;in, Indicates i The value vector after the extended search d Dimension value, represents the fifth random number between (0,1), Represents the sixth random number between (0,1).

2. The automobile logistics route allocation and scheduling method according to claim 1 is characterized in that: For the value vector after the extended search, a chaotic mapping search strategy is used to perform a spatial ergodic search on the value vector, and the value vector after the spatial ergodic search is obtained, including: Determine the chaotic mapping factor as: ;in, Indicates t +1 chaotic mapping factor during the optimization process, Indicates t The chaotic mapping factor in the sub-optimization process, mod represents the remainder function, represents the first constant term, and b =0.2; c represents the second constant term, and c =0.5; represents pi; According to the current optimization times, the nonlinear chaotic search control factor is determined as: ;in, r represents the nonlinear chaos search control factor, q represents the third constant term, and q =2; e represents a natural constant, T represents the maximum number of optimization times; For the value vector after the extended search, according to the chaotic mapping factor and nonlinear chaos search control factor r , perform a spatial ergodic search on the value vector, and the value vector after the spatial ergodic search is: ;in, Indicates t During the optimization process k The value vector after the extended search d Dimension value, k =1,2,...,I, I represents the total number of value vectors, Indicates k The value vector after the space traversal search d Dimension value, Indicates t The optimal value vector in the optimization process d dimension value, cos represents the cosine function, means (0,2 ) at random angles between .

3. The automobile logistics route allocation and scheduling method according to claim 2 is characterized in that: For the value vector after the spatial ergodic search, a random flight search strategy is used to perform a random flight search on the value vector, and the value vector after the random flight search is obtained, including: For any value vector after space traversal search, a corresponding random flight vector is generated using the Levy flight strategy; According to the random flight vector, a random flight search is performed on the value vector, and the value vector after the random flight search is obtained as follows: +| |* ;in, Indicates t During the optimization process m The value vector after the space traversal search d Dimension value, Indicates m The value vector after the random flight search d Dimension value, m =1,2,...,I, I represents the total number of value vectors, represents the random flight vector d Dimension value, represents the seventh random number between (0,1), cos represents the cosine function, Represents pi.

4. The automobile logistics route allocation and scheduling method according to claim 3 is characterized in that: For the value vector after random flight search, an adaptive weight search strategy is used to perform adaptive fine search on the value vector, and the value vector after adaptive fine search is obtained, including: According to the current optimization times, the adaptive weight is determined as: ;in, Indicates t The adaptive weight in the sub-optimization process, sin represents the sine function, represents pi, Indicates the maximum number of optimizations; According to the adaptive weight, an adaptive fine search is performed on the value vector, and the value vector obtained after the adaptive fine search is: ;in, Indicates t During the optimization process n The value vector after the random flight search d Dimension value, Indicates n The value vector after the adaptive fine search d Dimension value, represents the nonlinear chaos search control factor, Indicates t The optimal value vector in the optimization process d Dimension value, n =1,2,...,I, I represents the total number of value vectors, represents the eighth random number between (0,1), Represents the ninth random number between (0,1).

5. The automobile logistics route allocation and scheduling method according to claim 1 is characterized in that: Decoding the optimal value vector to obtain scheduling routes corresponding to multiple target logistics nodes, and scheduling automobile logistics based on the scheduling routes, including: Arrange the target logistics nodes in the optimal value vector in ascending order of value to obtain the optimal target logistics node sequence; When there is no need to return to the vehicle exit node, the vehicle exit node is added to the front of the optimal target logistics node sequence to obtain the scheduling routes corresponding to multiple target logistics nodes; When it is necessary to return to the vehicle exit node, the vehicle exit node is added to the front and back of the optimal target logistics node sequence to obtain the scheduling routes corresponding to multiple target logistics nodes; The automobile logistics is dispatched according to the dispatching route.

Images