CN114139392A - Vehicle path problem method and system based on self-adaptive large-scale field search algorithm - Google Patents

Vehicle path problem method and system based on self-adaptive large-scale field search algorithm Download PDF

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CN114139392A
CN114139392A CN202111471121.0A CN202111471121A CN114139392A CN 114139392 A CN114139392 A CN 114139392A CN 202111471121 A CN202111471121 A CN 202111471121A CN 114139392 A CN114139392 A CN 114139392A
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肖勇民
傅俊
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Shanghai Fawang Supply Chain Management Co ltd
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Abstract

The invention provides a vehicle path problem method and system based on a self-adaptive large-scale field search algorithm. The method comprises the following steps: constructing an initial solution by using a certain rule; selecting a run operator and an insert operator used in the iteration process based on the weight of the operator; executing run operation on the initial solution of the iteration, so that the initial solution becomes an infeasible solution; performing insert operation on the infeasible solution to obtain a feasible solution as much as possible; evaluating a feasible solution based on an optimization objective function, and determining whether to accept the feasible solution according to a preset strategy; and repeatedly executing the operation until the termination condition is reached, and returning the currently found optimal solution when the termination condition is reached. The vehicle path problem method and the vehicle path problem system based on the self-adaptive large-scale field search algorithm can solve the vehicle path problem.

Description

Vehicle path problem method and system based on self-adaptive large-scale field search algorithm
Technical Field
The invention relates to the technical field of big data, in particular to a vehicle path problem method and a vehicle path problem system based on a self-adaptive large-scale field search algorithm.
Background
The invention belongs to one of the most classical optimization problems in the field of logistics, and has great academic research significance and practical application value.
(1) Solutions to such problems in the academic and industrial circles are roughly divided into two categories, namely an accurate solution algorithm and a heuristic algorithm, the accurate solution algorithm is the most basic branch-and-bound algorithm, and although the optimal solution is guaranteed to be obtained within a limited time in theory, huge time is consumed in actual calculation.
(2) The idea of the heuristic algorithm is to construct and change a solution through a series of heuristic rules, thereby gradually improving the quality of the solution. Through continuous exploration and research, the meta-heuristic algorithm is proved to have better effect and efficiency in the aspect of solving the vrp. Some well-designed meta-heuristic algorithms, such as simulated annealing, tabu search, genetic algorithm, ant colony algorithm, adaptive large-scale field algorithm and the like, have very good performance in solving the vrp.
(3) In vehicle planning, aiming at minimizing the cost related to the vehicle and the driving distance, for the problem of practical scale, even if only hundreds of client nodes exist, the conventional optimization technology needs several days or even longer to find the optimal or near optimal solution, the transportation task of each time needs to determine the optimal transportation scheme in a short time, and the conventional method is difficult to implement in the practical logistics background.
Disclosure of Invention
The invention aims to provide a vehicle path problem method and a vehicle path problem system based on a self-adaptive large-scale field search algorithm, which can solve the vehicle path problem.
In order to solve the technical problem, the invention provides a vehicle path problem method based on a self-adaptive large-scale domain search algorithm, which comprises the following steps: constructing an initial solution by using a certain rule; selecting a run operator and an insert operator used in the iteration process based on the weight of the operator; executing run operation on the initial solution of the iteration, so that the initial solution becomes an infeasible solution; performing insert operation on the infeasible solution to obtain a feasible solution as much as possible; evaluating a feasible solution based on an optimization objective function, and determining whether to accept the feasible solution according to a preset strategy; and repeatedly executing the operation until the termination condition is reached, and returning the currently found optimal solution when the termination condition is reached.
In some embodiments, the initial solution, the infeasible solution, and the feasible solution have the form:
Figure BDA0003392277640000021
in some embodiments, the time of arrival of the vehicle at customer j is given by the following equation:
Figure BDA0003392277640000022
wherein, tijRepresents the travel time of the vehicle from client i to client j, i, j ∈ {0,1, …, N }; siRepresents the service (load/unload) of client i, i ∈ { i, …, N }; t is t0＝0；s0＝0。
In some embodiments, the problem to be solved is given by the following formula:
Figure BDA0003392277640000023
Figure BDA0003392277640000024
Figure BDA0003392277640000031
Figure BDA0003392277640000032
Figure BDA0003392277640000033
Figure BDA0003392277640000034
Figure BDA0003392277640000035
ai≤ti≤bi i∈{1，2，…，N} (10)
wherein d isijRepresenting the distance of i to customer j.
In some embodiments, equations (3), (4) represent the objective function, shortest path length, minimum number of vehicles, respectively.
In some embodiments, equation (5) indicates that the number of vehicles dispatched cannot exceed the number of vehicles owned by the central warehouse.
In some embodiments, equation (6) ensures that the vehicles are both departing from the warehouse and returning to the warehouse.
In some embodiments, equations (7), (8) ensure that each customer can only be serviced once by one vehicle.
In some embodiments, equation (9) defines a vehicle capacity constraint and equation (10) is a time window constraint.
In addition, the invention also provides a vehicle path problem system based on the self-adaptive large-scale domain search algorithm, which comprises the following steps: one or more processors; storage means for storing one or more programs; when executed by the one or more processors, cause the one or more processors to implement a vehicle path problem method based on the adaptive large scale domain search algorithm as described above.
After adopting such design, the invention has at least the following advantages:
different types of optimized target equations and constraint conditions are summarized through abstraction of business problems, a run operator and an insert operator are introduced, iterative solution is carried out on the target equations, and the solution of the vrp is achieved.
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The foregoing is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and the detailed description.
FIG. 1 is a method flow diagram.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The run algorithm is a basic greedy algorithm. The removed customers are continually added back to cheapest (the location that raises the objective function the least). The specific method comprises the following steps:
we define this variable of the objective function after inserting customer i into route k where the objective function is least elevated. When vehicle i cannot be inserted into route k, we have. And then calculating, and continuously inserting i into route k. It is true that the vehicle that can minimize the increase of the objective function is selected from all the vehicles to be serviced. The above operations are continuously performed until all customers are inserted back into the solution, or there is no place where insertion is possible. It is worth noting here that all trains can be tabulated to reduce time complexity and avoid recalculation every time.
Insert operators are the process of improving solutions, which are so long that the vehicle that contributes most in the objective function often needs to be considered for relocation. In implementation, we define that the value of the objective function increases (decreases) after point i is completely removed from the solution. Sorting all points by value, the probability that a vehicle in front is selected is greater.
The logistics vehicle path problem is more complicated, relates to a large amount of vehicles, and the distribution routing path optimization of personnel, along with the further development of logistics industry, the continuous expansion of demand network points, the requirement on an intelligent planning algorithm is also higher, and the method is exquisite in effectiveness, accuracy and the like.
(1) The operation of vehicle path planning when falling to the society can not only generate huge social value, but also generate huge scientific research value on the basis. Firstly, the transportation cost is saved, the operation efficiency is improved, decision support is provided for enterprises, secondly, the vrp problem well embodies that academic research and practical application are complementary, and with the application and popularization of operational research in the logistics field, the vigorous development of the logistics industry is further the academic development of the related fields in China.
(2) The method has the advantages that compared with other algorithms, firstly, the framework is easy to expand, except for the vrp problem, the method can also solve the variant problems of vrp pd, mdvrp and the like, in addition, the problems of different types can have stable and good solving results, and finally, the method is convenient for upgrading and expanding the algorithm.
Scene description: there are several customers who have certain demands for certain goods, and the vehicles can be delivered to the customers after being taken from the warehouse, and the customers and the warehouse form a delivery network, and the vehicles can move in the network to complete delivery tasks, so as to minimize the number of used vehicles and the total travel distance of the vehicles (generally, minimize the number of vehicles).
The modeling is as follows: warehouse number 0, customer number 1, 2, …, N, define variables.
Figure BDA0003392277640000051
Time of arrival of vehicle at customer j:
Figure BDA0003392277640000052
in the formula, tijRepresents the travel time of the vehicle from client i to client j, i, j ∈ {0,1, …, N }; siRepresents the service (load/unload) of client i, i ∈ { i, …, N }; t is t0＝0；s0＝0。
The mathematical model is represented as follows:
Figure BDA0003392277640000061
Figure BDA0003392277640000062
Figure BDA0003392277640000063
Figure BDA0003392277640000064
Figure BDA0003392277640000065
Figure BDA0003392277640000066
Figure BDA0003392277640000067
ai≤ti≤bi i∈{1，2，…，N} (10)
in the formula: dijRepresenting the distance of i to customer j.
In the model, the equations (3) and (4) represent an objective function, which is the shortest path length and the minimum vehicle number respectively; equation (5) represents that the number of vehicles dispatched cannot exceed the number of vehicles owned by the central warehouse; equation (6) ensures that the vehicles are all from the warehouse and returned to the warehouse; equations (7), (8) ensure that each customer can only be serviced once by one vehicle; equation (9) defines a vehicle capacity constraint; equation (10) is a time window constraint.
The ALNS algorithm mainly comprises the following steps:
constructing an initial solution by using a certain rule;
selecting a Ruin operator and an Insert operator used in the iteration process based on the weight of the operator;
performing run operation on the initial solution of the iteration, namely deleting part of client points which are already served by the vehicle, so that the initial solution becomes an infeasible solution;
performing an Insert operation on the solution obtained in the step (3), namely inserting the solution into a customer point which is not served by the vehicle, and obtaining a feasible solution as much as possible;
based on the new solution obtained in the step (4) of evaluating the optimization objective function, and determining whether to accept the new solution according to a certain strategy;
and judging whether a termination condition is reached. If yes, stopping calculation, and returning to the best solution found currently; otherwise, updating the weight of the operator based on the expression of the operator in the calculation of the round, and returning to the step (2)
The flow chart is shown in fig. 1.
The invention has the following key points:
the functions are perfected: on the basis of the original algorithm core framework, the support of the types of problems such as vehicle picking and delivering while vehicle delivering, vehicle delivering in multiple passes and the like is added. Through abstraction of business problems, different types of optimization objective equations are summarized (e.g., minimizing total cost of ladder pricing, minimizing delivery time, etc.) and constraints (e.g., vehicle travel distance limits, vehicle delivery order number limits, vehicle cross-zone number limits, etc.).
Operator enrichment: in order to improve the solving result and the stability of the engine, richer optimization operators are added.
Aiming at the problem of ultra-large scale, firstly clustering the customer points through a proper clustering algorithm so as to decompose the original problem into a plurality of sub-problems of small scale, then solving in parallel, and finally decomposing the sub-problems into the solution of the original problem.
And (3) parallelization upgrading of the algorithm: most heuristic algorithms can improve the search efficiency and effect through parallel calculation, the parallel calculation evaluates the quality of a plurality of adjacent solutions, searches towards a plurality of field directions or searches by using a plurality of strategies, and even searches by using a plurality of algorithms in parallel.
The invention also provides a vehicle path problem system based on the self-adaptive large-scale field search algorithm. For example, the vehicle path problem system based on the adaptive large-scale domain search algorithm can be used for a vehicle path problem solving host machine in a big data analysis system. As described herein, a vehicle routing problem system based on an adaptive large-scale domain search algorithm may be used to implement a solution function for a vehicle routing problem in a big data analysis system. The vehicle path problem system based on the adaptive large-scale domain search algorithm may be implemented in a single node, or the functions of the vehicle path problem system based on the adaptive large-scale domain search algorithm may be implemented in multiple nodes in the network. Those skilled in the art will appreciate that the term adaptive large scale domain search algorithm based vehicle path problem system includes devices in a broad sense, and that the adaptive large scale domain search algorithm based vehicle path problem system referred to herein is but one example. The inclusion of an adaptive large-scale domain search algorithm-based vehicle routing problem system is for clarity and is not intended to limit the application of the present invention to a particular adaptive large-scale domain search algorithm-based vehicle routing problem system embodiment or to a certain class of adaptive large-scale domain search algorithm-based vehicle routing problem system embodiments. At least some of the features/methods described herein may be implemented in a network device or component, such as a vehicle path problem system based on an adaptive large-scale domain search algorithm. For example, the features/methods of the present invention may be implemented in hardware, firmware, and/or software running installed on hardware. The vehicle path problem system based on the adaptive large-scale domain search algorithm may be any device that processes, stores and/or forwards data frames through a network, such as a server, a client, a data source, etc. A processor may include one or more multi-core processors and/or memory devices, which may serve as data stores, buffers, and the like. The processor may be implemented as a general-purpose processor, or may be part of one or more Application Specific Integrated Circuits (ASICs) and/or Digital Signal Processors (DSPs).
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the present invention in any way, and it will be apparent to those skilled in the art that the above description of the present invention can be applied to various modifications, equivalent variations or modifications without departing from the spirit and scope of the present invention.

Claims (10)

1. A vehicle path problem method based on a self-adaptive large-scale domain search algorithm is characterized by comprising the following steps:
constructing an initial solution by using a certain rule;
selecting a run operator and an insert operator used in the iteration process based on the weight of the operator;
executing run operation on the initial solution of the iteration, so that the initial solution becomes an infeasible solution;
performing insert operation on the infeasible solution to obtain a feasible solution as much as possible;
evaluating a feasible solution based on an optimization objective function, and determining whether to accept the feasible solution according to a preset strategy;
and repeatedly executing the operation until the termination condition is reached, and returning the currently found optimal solution when the termination condition is reached.
2. The adaptive large-scale domain search algorithm-based vehicle path problem method according to claim 1, wherein the initial solution, the infeasible solution, and the feasible solution have the form:
Figure FDA0003392277630000011
3. the vehicle path problem method based on the adaptive large-scale domain search algorithm according to claim 1, wherein the time for the vehicle to reach the client j is given by the following formula:
Figure FDA0003392277630000012
wherein, tijRepresents the travel time of the vehicle from client i to client j, i, j ∈ {0,1, …, N }; siRepresents the service (load/unload) of client i, i ∈ { i, …, N }; t is t0＝0；s0＝0。
4. The vehicle path problem method based on the adaptive large-scale domain search algorithm according to claim 1, wherein the problem to be solved is given by the following formula:
Figure FDA0003392277630000021
Figure FDA0003392277630000022
Figure FDA0003392277630000023
Figure FDA0003392277630000024
Figure FDA0003392277630000025
Figure FDA0003392277630000026
Figure FDA0003392277630000027
ai≤ti≤bi i∈{1，2，…，N} (10)
wherein d isijRepresenting the distance of i to customer j.
5. The vehicle routing problem method based on the adaptive large-scale domain search algorithm according to claim 4, wherein equations (3) and (4) represent objective functions, which are shortest path length and minimum number of vehicles, respectively.
6. The adaptive large-scale domain search algorithm-based vehicle path problem method according to claim 4, wherein the expression (5) represents that the number of dispatched vehicles cannot exceed the number of vehicles owned by the central warehouse.
7. The adaptive large-scale domain search algorithm-based vehicle path problem method according to claim 4, wherein equation (6) ensures that all vehicles are going from the warehouse and going back to the warehouse.
8. The vehicle path problem method based on the adaptive large-scale domain search algorithm according to claim 1, wherein equations (7) and (8) ensure that each customer can be serviced by only one vehicle once.
9. The adaptive large-scale domain search algorithm-based vehicle path problem method according to claim 1, wherein equation (9) defines a vehicle capacity constraint and equation (10) is a time window constraint.
10. A vehicle path problem system based on an adaptive large-scale domain search algorithm is characterized by comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the vehicle path problem method based on the adaptive large scale domain search algorithm of any of claims 1 to 9.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115185303A (en) * 2022-09-14 2022-10-14 南开大学 Unmanned aerial vehicle patrol path planning method for national parks and natural protected areas
CN116629586A (en) * 2023-07-24 2023-08-22 青岛民航凯亚系统集成有限公司 Airport guarantee vehicle scheduling method and system based on ALNS

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115185303A (en) * 2022-09-14 2022-10-14 南开大学 Unmanned aerial vehicle patrol path planning method for national parks and natural protected areas
CN116629586A (en) * 2023-07-24 2023-08-22 青岛民航凯亚系统集成有限公司 Airport guarantee vehicle scheduling method and system based on ALNS

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