Application And Improvement Of Particle Swarm Algorithm In Supply Chain Network

In this paper,we mainly study the supply chain from two aspects: the improvement of the model and the algorithm solution.The main contents are as follows:(1)The network structure of supply chain is analyzed,and a three layer determine demand supply chain network model is established to under single commodity flow.The optimization conditions for manufactures,retailers and demand markets are given respectively,and then the variational inequalities for the entire supply chain network under equilibrium condition are obtained.In consideration of the modified projection method relies heavily on the Lipschitz constant,iterative step,two projection calculation difficulties,so we using improved particle swarm optimization algorithm to solve the problem.Through four numerical examples,and comparing with the standard particle swarm algorithm,artificial bee colony algorithm,and the particle swarm optimization algorithm with synchronous learning factor,the improved particle swarm optimization algorithm is proved to be effective and superior in solving the supply chain network equilibrium problem.(2)Considering the actual production needs,companies often face the factors of random demands,the time of delivery between two companies,and the diversification of commodities in the market.So we set up a multi-commodity flow random supply chain network model that manufacturers promise delivery time.The optimization conditions for manufactures,retailers and demand markets are given respectively,and then the variational inequalities for the entire supply chain network under equilibrium condition are obtained.Through two examples,we use the improved particle swarm optimization algorithm to calculate,and compare the convergence speed and accuracy with the other three algorithms.The result of the numerical example prove the rationality of the model.