Bacterial Foraging Optimization Algorithm For Solving The Problem Of Logistics Network

The development of Internet technology,it improves the advance of the logistics industry,and raises people's consumption level at the same time.The development of the logistics industry marks the modernization level of a country.The main competition of logistics industry is reflected in the advantages and disadvantages of logistics network planning.The three main factors that constitute the logistics network are facility location,route arrangement and inventory control for the service target.Under the emphasis of the concept of "logistics integration",the logistics network system needs to be integrated and optimized for the logistics network system,which is the Combined Location Routing and Inventory Problem(CLRIP).In the logistics network planning,the decision-makers pay most attention to the cost of the whole system,and the customer and distribution facilities are more concerned about the response efficiency of the whole system,that is the time problem.Therefore,on the premise of considering the cost and time in the logistics network planning,this paper studies the location,route arrangement and inventory control of the logistics network.Firstly,it introduces the research background,research significance and research content of the topic.By introducing and analyzing the basic components of the logistics network system such as the Vehicle Routing Problem(VRP),Location-Routing Problem(LRP),Location-Allocation Problem(LAP),and the domestic and foreign research status of the above problems,the citation points out the importance of studying the integration problem of CLRIP.This paper also reviews the recent research on CLRIP in China and abroad.Secondly,the core algorithm of the research content: Bacteria Foraging Optimization(BFO),the basic theory of the BFO and its main operations: Chemotaxis,Reproduction and Elimination algorithm process.The advantages and disadvantages of BFO compared with other intelligent algorithms are briefly summarized,as well as the improvement and application of BFO algorithm in recent years.Thirdly,the multi-objective optimization problem and research status are briefly introduced.Considering the importance of time and cost factors in the process of fresh logistics distribution,the idea of multi-objective optimization is adopted to improve the basic BFO,and a multi-objective bacterial foraging optimization algorithm based on Pareto dominating idea is designed,it also solves the problem of Location Routing Problem(LRP)with soft time windows which meets the fresh logistics distribution problem.Through the solution and simulation of the model,it is proved that the proposed algorithm has a certain feasibility and advantage in solving such problems,and it also lays the foundation for the subsequent study of the CLRIP model of bilevel programming.Finally,based on the bilevel programming idea,based on the previous research content,taking into account the order cycle of the distribution point on the same path,it is considered as a new decision variable.Under the premise of ensuring the minimum total cost of the inventory,the optimal order quantity and order period of each distribution route are determined,and a corresponding bilevel programming CLRIP model is established.The initial allocation of distribution points is first allocated by the outer layer algorithm to ensure the minimum total inventory cost,that is,the Location Allocation Problem(LAP),and then through the inner algorithm to arrange and optimize the initial scheme solved by the outer algorithm,that is,the Location Routing Problem(LRP),to solve the problem through internal and external nesting.The advantages and effectiveness of the algorithm are verified by corresponding examples.Through the study of the problem model designed,the shortcomings of the existing literature on LRP and CLRIP research are further studied and optimized.At the same time,the improvement of the basic BFO algorithm also provides a certain way of thinking for the practical application of the BFO algorithm in solving the multi-objective optimization problem.