Solving Location-routing Problem Of Cold Chain Logistics Based On Improved Bacterial Foraging Optimization Algorithm

Under the guidance of the Internet,the field of e-commerce has developed rapidly.The ensuing logistics and distribution problems have brought huge challenges to the entire logistics industry.In recent years,with the increase in the production of cold chain commodities in my country,how to increase the speed of logistics distribution,the quality of distribution services,and reduce logistics costs has become a hotspot in the entire logistics field.This paper analyzes the current problems in cold chain logistics distribution,establishes a site selection-distribution model in cold chain logistics,and based on scholars' research on site selection distribution problems,proposes the use of tabu search algorithm and improved bacterial foraging optimization algorithm interactive Solve the problem.The main work done in this article is as follows:(1)Compared with other logistics distribution,cold chain logistics distribution has higher requirements for timeliness.This paper studies the problem of vehicle path planning with time window constraints.To solve the problem of vehicle path planning with time window constraints,an improved bacterial foraging optimization algorithm is proposed.The CW algorithm and the greedy strategy insertion method are used to construct the initial solution of the improved bacterial foraging optimization algorithm;the characteristics of the neighborhood transformation operator commonly used in vehicle path planning are analyzed,combined with the time window factor,and the basic design is based on the relocate neighborhood transformation operator.m-relocate operator,minimum number of customers m-relocate operator,long-path m-relocate operator and cost reduction maximum m-relocate operator.Combining these four neighborhood transformation operators with the chemotaxis operation in the bacterial foraging optimization algorithm,the m in the m-relocate operator is used as the step size of the bacterial swim,and the four neighborhood operators are used as the bacteria swim.The direction improves the optimization efficiency of the bacterial foraging optimization algorithm.The improved bacterial foraging optimization is simulated by 56 examples in the Solomon dataset,and the experimental results are compared with the traditional two-stage method,PITSH,improved ant colony algorithm and bee colony algorithm.The improved bacterial foraging optimization algorithm has advantages in solving vehicle routing problems with time window constraints.(2)To analyze the main costs influencing the cold chain logistics site selection and distribution process,the bi-level programming model is used to split the cold chain logistics site selection and distribution problem into site allocation and multi-distribution center vehicle routing rules with time window constraints.It is proposed to use tabu search algorithm and improved bacterial foraging optimization algorithm to interactively solve the location anddistribution problem in cold chain logistics.In the process of using the tabu search algorithm to solve the location allocation problem,according to the characteristics of the location allocation problem with time window constraints,a K-means clustering algorithm considering the time window factor was designed.In this method,a K-means clustering algorithm was designed.Considering the distribution of customers in the geographic location and combining the similarity function of the customer's required delivery time window,the location of the alternative distribution center is used as the initial cluster center of the K-means clustering algorithm,and the number of alternative distribution centers is used as the cluster.For the K value of the class algorithm,the result of clustering the K-means algorithm considering the time window factor is used as the initial solution of the tabu search algorithm.In the improved bacterial foraging optimization algorithm to solve the multi-distribution center vehicle routing problem with time window constraints,integer coding is used to decode the multi-distribution center vehicle routing problem with time window constraints.In this coding,the number of all alternative distribution centers and the number of distribution center vehicles in the multi-distribution center vehicle routing problem with time window constraints are taken into account,which facilitates the transformation of the neighborhood operator and helps to improve the search speed of the algorithm.The upper level tabu search algorithm is used to obtain the solution of the location allocation problem.The greedy strategy insertion method is used to construct the initial solution of the multi-distribution center vehicle routing problem with time window constraints.In the lower layer,the improved bacterial foraging optimization algorithm is used to obtain the time window Constrained multi-distribution center vehicle routing problem is solved by the solution of the location allocation problem,and the resolved location allocation problem is used as the initial solution of the tabu search algorithm.Chain logistics site selection-optimal solution to the distribution problem.Finally,this paper selects the R101 example in the Solomon data set as the customer in the cold chain logistics location-distribution problem,randomly generates 15 alternative distribution centers,and combines the cold chain logistics distribution parameters in the relevant literature to construct the cold-chain logistics location-distribution Test example of the problem.A tabu search algorithm and an improved bacterial foraging optimization algorithm were used to solve the example interactively to verify the effect of the interactive method for the cold chain logistics location-distribution problem.