Research On Vehicle Routing Problem With Time Window And Energy Consumption In Cold Chain Logistics

In June 2019,the National Development and Reform Commission(NDRC)advocated the implementation of a green and efficient refrigeration program and enhanced awareness of green energy conservation.As a kind of industry closely related to people's life,the logistics distribution industry should attach importance to green energy conservation and better promote the implementation of energy conservation measures.In order to reduce transportation costs,improve customer service quality and reduce energy consumption,finding the best route for vehicle delivery is a canonical Vehicle Routing Problem(VRP)and it is also a very important decision-making problem.In this paper,we consider a novel Heterogeneous Vehicle Routing Problem(HVRP)--the vehicle routing problem with time window and energy consumption in the cold chain.The distribution vehicles are divided into two types: ordinary vehicles and refrigerated vehicles,the goal is to minimize the total transportation cost while considering customer satisfaction,so a new problem model is established,and an improved artificial fish swarm algorithm is used to solve this kind of problem.Finally,the effectiveness of the improved algorithm is proved by a large number of simulation experiments.The main contents are as follows:(1)Establishment of the problem model.The domestic and foreign references about VRP and its variants in recent years were consulted,the development trend of vehicle routing problem was analyzed,and the cold chain logistics problem of multiple models was modeled.The research objective of heterogeneous cold chain logistics problem is to minimize the total transportation cost under the constraint of satisfying each customer's demand and time window.(2)Problem-driven heuristic strategy.Initialization strategy: several initialization strategies for the vehicle routing problem with time window are analyzed and studied in the existing literature,while considering the impact on the current customer and the time window constraint of the newly inserted customer,an improved initial solution generation strategy is used to generate a higher quality solution,which not only ensures the quality of the initial solution,but also improves the diversity of the initial population.Vehicle waiting strategy: in order to further improve the performance of the algorithm,a vehicle waiting strategy is designed to improve customer satisfaction without increasing energy consumption.(3)Improvement of artificial fish swarm algorithm.Firstly,the encoding and decoding are improved to ensure that there is no need to repair the encoding and decoding in the subsequent operation.In order to make the algorithm more suitable for solving discrete optimization problems,the preying behavior and following behavior are improved.The improved following behavior learns the optimal solution within a certain probability range and has the ability to jump out of the local optimal solution.At the same time,the improved artificial fish swarm algorithm combines mutation operators and crossover operators to enhance the local and global search capabilities.The random behavior can be learned from the neighborhood,which further enhances the searching ability of the algorithm.At the end of the paper,an extended SOLOMON example is used to test the improved algorithm,and the effectiveness of the proposed algorithm is fully verified through detailed experimental comparison.