CN-122022301-A - Cold chain two-stage heterogeneous multi-cabin vehicle scheduling method considering soft time window

CN122022301ACN 122022301 ACN122022301 ACN 122022301ACN-122022301-A

Abstract

The invention discloses a cold chain two-stage heterogeneous multi-cabin vehicle scheduling method considering a soft time window, and belongs to the field of cold chain logistics transportation management and path optimization. On one hand, the invention builds a mathematical model aiming at minimizing the total cost, wherein the total cost comprises transportation cost, fixed departure cost and delay penalty cost, on the other hand, vehicle type selection is carried out by introducing the ratio of the minimum normalized total cost to the loading capacity, and a plurality of targeted destruction and repair operators are designed by combining the problem characteristics so as to effectively improve the searching capacity of the algorithm and the quality of solutions. The method can effectively solve the problem of heterogeneous vehicle resource allocation, and can remarkably reduce the operation cost of cold chain logistics while rapidly obtaining the global optimal solution.

Inventors

GUO NING
LIN MINGFENG
WANG BIN
QIAN BIN
NA JING
HU RONG

Assignees

昆明理工大学

Dates

Publication Date: 20260512
Application Date: 20260123

Claims (10)

1. A method for scheduling a cold chain two-stage heterogeneous multi-cabin vehicle taking soft time windows into account, comprising: Step1, constructing a cold chain two-stage heterogeneous multi-cabin vehicle dispatching optimization model considering a soft time window according to a cold chain two-stage heterogeneous multi-cabin vehicle transportation process, wherein in a first-stage transportation network, each first-stage vehicle starts from a cargo collection center and returns to the cargo collection center after providing service for each transfer station, and in a second-stage transportation network, each second-stage vehicle starts from a transfer station and returns to a corresponding transfer station after providing service for a second-stage customer in the coverage area of the transfer station; step2, setting main parameters; Step3, distributing clients to different transfer stations by using a K-means algorithm to obtain a transfer station client distribution result; Step4, constructing an initial solution for the first-stage transport network and each second-stage transport network to obtain a first-stage solution and each transfer station solution; Step5, selecting an adaptive vehicle type from each level of vehicle fleet according to the first-level solution and each transfer station solution and combining a vehicle type distribution method, constructing a first-level solution with the vehicle type and each transfer station with the vehicle type as a complete solution, and calculating the initial total cost of the complete solution according to an objective function; Step6, selecting a destruction operator and a repair operator in a roulette manner, and sequentially executing destruction and repair operations on the first-stage solution and each transfer station solution to generate a candidate solution; Step7, re-executing the vehicle type distribution on the new solution according to the vehicle type distribution method of Step5, calculating the total cost of the new complete solution, and judging whether to accept the new solution as the current solution of the next generation according to a preset acceptance criterion; step8, dynamically updating the scores and weights of the destruction operator and the repair operator according to the improvement condition of the solution; Step9, judging whether the scheduling termination condition is met, if yes, exiting the loop and outputting a global optimal solution, and if not, returning to execute Step6 and continuing the next iteration.
2. The method for scheduling cold chain two-stage heterogeneous multi-cabin vehicles taking soft time windows into consideration according to claim 1, wherein the clients are distributed to different transfer stations by using a K-means algorithm, and the method comprises the following specific steps: Step3.1, setting a corresponding number of client groups according to the number of transfer stations, and setting the coordinates of each transfer station as the initial center of gravity of each client group; step3.2, calculating the Euclidean distance between each customer and the gravity center of each customer group; Step3.3, assigning the client group with the minimum Euclidean distance to the client, wherein if the client is assigned to the nearest client group and does not exceed the upper limit of the capacity of the corresponding transfer station, the assignment is confirmed; step3.4, updating the gravity centers of all client groups after the distribution of all clients is completed; step Step3.5, judging whether the distribution termination condition is reached, if so, outputting a final customer distribution result, otherwise, returning to step3.2 to continue the next iteration.
3. The cold chain two-level heterogeneous multi-cabin vehicle scheduling method considering the soft time window according to claim 1, wherein the Step4 is to construct an initial solution for the first-level transportation network, and the specific steps are as follows: s4.1.1, for the primary transportation network, taking the collection center as a starting point and constructing an ending point pair A path, wherein, the path is a path, Representing the total number of transfer stations in the primary transport network; s4.1.2, calculating the saving value between two transfer station nodes according to the distance between the goods collection center and any two transfer station nodes, and arranging the obtained results in a descending order to form a first saving value list; S4.1.3, traversing the first saving value list, namely selecting a node pair with the maximum saving value each time, if two transfer station nodes in the current node pair belong to two different paths and are all head and tail transfer station nodes in the paths, merging the two different paths into one path, otherwise, continuing traversing until all node pairs of the first saving value list are processed, and forming a plurality of first-stage transportation routes taking a collection center as a starting point as a first-stage solution.
4. The cold chain two-level heterogeneous multi-cabin vehicle scheduling method considering the soft time window according to claim 1, wherein the Step4 is to construct an initial solution for each two-level transportation network, and the specific steps are as follows: S4.2.1 for the secondary transport network, constructing by taking each transfer station as a starting point and an ending point A path; Representing the number of transfer station clients obtained according to the distribution result of the transfer station clients; S4.2.2, calculating the saving value between two client nodes according to the distance between the transfer station and any two client nodes, and arranging the obtained results in a descending order to form a second saving value list; S4.2.3 traversing the second saving value list, selecting a node pair with the largest saving value each time, combining two different paths into one path if two client nodes in the current node pair belong to two different paths and are all head and tail client nodes in the paths, otherwise, continuing traversing until all node pairs of the second saving value list are processed, and forming a plurality of secondary transportation routes taking a transfer station as a starting point and a stopping point as a transfer station solution.
5. The cold chain two-stage heterogeneous multi-cabin vehicle scheduling method considering soft time windows according to claim 1, wherein the vehicle type allocation method is as follows: for the current solution, determining a node sequence of one-time performance service of each vehicle type as a subsequence according to the capacity of each vehicle type of the transport network in which the current solution is positioned, wherein the current solution is a first-order solution or a solution of each transfer station; according to the subsequence, calculating the CCR value of each vehicle type, and selecting the vehicle type with the minimum CCR value as a service vehicle type to serve the subsequence of the service vehicle type until the vehicle type distribution of the node sequence in the current solution is completed, wherein the CCR value represents the ratio of the normalized total cost of the vehicle type to the service subsequence to the loading capacity of the vehicle type.
6. The method for scheduling cold chain two-stage heterogeneous multi-cabin vehicles taking soft time windows into consideration according to claim 1, wherein the destruction operators comprise a random removal operator, a worst cost removal operator, a worst penalty cost removal operator, a distance correlation removal operator and a demand correlation removal operator; The repair operator comprises a random insertion operator, an regrettably value insertion operator, a greedy insertion operator based on penalty cost, a greedy insertion operator based on total cost and a greedy insertion operator based on distance.
7. The cold chain two-stage heterogeneous multi-cabin vehicle scheduling method considering a soft time window according to claim 1, wherein the scores of a destruction operator and a repair operator are dynamically updated according to the improvement condition of a solution, specifically; when the new solution is better than the global optimal solution, determining that the corresponding selected destructive operator and repair operator take a first score; When the new solution is not better than the global optimal solution but is better than or equal to the current solution, determining that the corresponding destruction operator and repair operator take a second score; When the new solution is inferior to the current solution but still accepted by the algorithm according to the acceptance criterion, determining that the corresponding destruction operator and repair operator take a third score; When the new solution is inferior to the current solution and is rejected by the algorithm according to the acceptance criteria, determining that the corresponding destruction operator and repair operator take a fourth score.
8. The cold chain two-stage heterogeneous multi-cabin vehicle scheduling method considering a soft time window according to claim 1, wherein the weights of the destruction and repair operators are dynamically updated according to the improvement condition of the solution, specifically: ; ; In the formula, Representing a set of all the destruction and repair operators, And (3) with Representing operators respectively At the current stage And the next stage Is used for the weight of the (c), Is an operator At the current stage The cumulative score of the score is calculated, Is an operator At the current stage The number of calls within the time frame, Is a response factor; Representing an initial score; Represent the first A score.
9. A processor for running a program, wherein the program when run performs the steps of the cold chain two-level heterogeneous multi-cabin vehicle scheduling method of any one of claims 1-8 that takes into account soft time windows.
10. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored program, wherein the program when run controls a device in which the computer readable storage medium is located to perform the steps of the cold chain two-level heterogeneous multi-cabin vehicle scheduling method taking into account the soft time window according to any one of claims 1-8.

Description

Cold chain two-stage heterogeneous multi-cabin vehicle scheduling method considering soft time window Technical Field The invention relates to a cold chain two-stage heterogeneous multi-cabin vehicle scheduling method considering a soft time window, and belongs to the field of cold chain logistics transportation management and path optimization. Background With the vigorous development of the fresh electronic commerce industry and the improvement of the consumption level, consumers have increasingly stringent requirements on the freshness of foods, and the cold chain logistics is taken as a key link for guaranteeing the quality of the foods, so that the market scale of the food is rapidly expanded. However, fresh products are various, different types of goods have different requirements on storage temperature, and the conventional single-temperature-zone vehicle is difficult to meet the transportation requirement of multi-product mixed loading, so that the distribution efficiency is low. For this reason, multi-cabin vehicles are introduced into logistics systems, and in order to pursue the maximization of transportation benefit, multi-cabin vehicles are generally designed as large trucks with high load and long body, but this results in limitation to urban traffic control and complicated road conditions, and difficulty in directly carrying out distribution service to customers, so that the construction of a secondary transportation network becomes a necessary choice, that is, primary transportation is completed by using large vehicles, and customer service is completed by small vehicles. However, if only a single-specification homogenization fleet is configured in each transportation link, the system is difficult to meet the demands of differentiated orders, and the loading rate is low due to the delivery of small orders by large-capacity vehicles, and the dispatching frequency and the driving mileage are increased due to the response of large-capacity vehicles to large-capacity orders. In order to balance efficiency and cost, it is particularly necessary to introduce heterogeneous fleets of different capacities. In a vehicle type distribution link, a vehicle type selection method for normalizing the ratio of total cost to loading capacity is provided, and is used for realizing efficient configuration of transport capacity resources and reducing operation cost. In addition, the customer delivery time in actual delivery is often flexible, and the hard time window is too tight, which tends to limit the flexibility of the scheduling scheme and results in increased operating costs. For this reason, a soft time window is introduced, time deviation is converted into penalty cost, and distribution cost is reduced while service level is ensured. In conclusion, the method has important theoretical value and practical significance in researching the scheduling problem of the cold chain two-stage heterogeneous multi-cabin vehicle considering the soft time window. The problem combines complex constraints of multiple cabins, secondary, soft time windows, heterogeneous fleets and the like, and belongs to a typical NP-hard problem. The solution space is huge and the calculation complexity is high, so that the design of a targeted efficient algorithm is a key for obtaining a high-quality solution in a limited time. Disclosure of Invention The invention provides a cold chain two-stage heterogeneous multi-cabin vehicle scheduling method considering a soft time window, which is characterized in that a mathematical model aiming at minimizing total cost is constructed, wherein the total cost comprises transportation cost, fixed departure cost and delay penalty cost, on the one hand, vehicle type selection is carried out by introducing the ratio of minimum normalized total cost to loading capacity, and a plurality of targeted destruction and repair operators are designed by combining problem characteristics, so that the searching capacity of the algorithm and the quality of solution are effectively improved. The method can effectively solve the problem of heterogeneous vehicle resource allocation, and can remarkably reduce the operation cost of cold chain logistics while rapidly obtaining the global optimal solution. The technical scheme of the invention is as follows: According to a first aspect of the present invention, there is provided a cold chain two-level heterogeneous multi-cabin vehicle scheduling method considering a soft time window, comprising: Step1, constructing a cold chain two-stage heterogeneous multi-cabin vehicle dispatching optimization model considering a soft time window according to a cold chain two-stage heterogeneous multi-cabin vehicle transportation process, wherein in a first-stage transportation network, each first-stage vehicle starts from a cargo collection center and returns to the cargo collection center after providing service for each transfer station, and in a second-stage transportation netwo