Integrated Supply Chain Optimization Based On Multi-objective Bacterial Foraging Optimization

With the development of information technology and the global economy,the integrated management of supply chain has become an important way for enterprises to reduce costs and improve customer satisfaction.By setting up strategic alliances with the upstream and downstream enterprises in the supply chain,enterprises share information to promote the timely supply of raw materials,the normal production of products and the delivery of finished goods in a timely manner.As the important sectors in the supply chain,saying that procurement,production and distribution,the coordination and optimization of this three becomes an effective way to reduce operating costs and improve the core competitiveness of enterprises.Supported by National Nature Science Foundation(71571120,71271140,71471158)and Guangdong Province Nature Science Foundation(2016A030310074),this paper's researches are as follows:This paper studies the three-echelon procurement-production-distribution supply chain model.It considers the cost of multiple suppliers,single manufacturer and multiple distributors in a multi-echelon supply chain,and constructs an integrated multi-period and multi-product supply chain model.The decision variables are the procurement quantity of raw materials from each supplier to the manufacturer in each period,quantity of different products produced in the manufacturer in each period,quantity of different products distributed from the manufacturer to each distributor in each period.The model is intended to minimize the total cost of the supply chain and the total number of backorders,namely to meet the needs of customers as far as possible while reducing the operation cost,and it considers multiple constraints at the same time,such as inventory capacity,production lead time,etc.Therefore,this paper studies a complex multi-objective and multi-constrained NP-hard problem.As a new swarm intelligence algorithm,bacterial foraging optimization has attracted the attention of many scholars and has been widely used in many fields successfully.Based on the standard bacterial foraging optimization algorithm,this paper proposes an improved algorithm named Cooperative Multi-objective Bacterial Foraging Optimization.This paper introduces the cooperative evolutionary mechanism,which effectively improves the search efficiency of bacteria.It also introduces non-dominated sorting and external archives to deal with multiple objectives,comparing the solutions founded by the bacteria and saving the non-dominated solutions found by the bacteria in external archives.This paper also proposes a mechanism to control the feasibility of solutions,ensuring that the solutions found by bacteria are in the feasible region.In addition,the nested loop in the standard algorithms is simplified to a single general loop by redesigning the algorithm structure.It leads to the reduction of the memory consumption and complexity.In addition,.In order to test the effectiveness of the proposed algorithm,the paper introduces five common multi-objective test functions for testing,and compares with other common multi-objective intelligent algorithms.Based on the proposed multi-objective bacterial foraging algorithm,this paper encodes the bacteria,so that each bacteria represents a solution to solve the multi-objective integrated supply chain model.The simulation results show the effectiveness of the proposed multi-objective bacterial foraging algorithm in solving multi-objective problems.