Collaboration Scheduling Method For Manufacturing Chain

Manufacturing chain collaboration scheduling is a new problem for management and control of modern enterprises under currently globalization,network environment.Particle swarm optimization algorithm is based on swarm intelligence theory;it provides new and highly efficient solutions for the scheduling of such complex optimization problems.In this paper,based on improved particle swarm optimization algorithm to solve cooperative scheduling and optimization problem between each node of manufacturing chain,the purpose of this study is according to capacity constraints of system and real-time information in manufacturing process,to coordinate and scheduling the production process and beat of each upstream and downstream node on the manufacturing chain,achieve the overall performance optimization of the manufacturing chain.Because of the complexity of the manufacturing chain collaboration scheduling problem,it will be divided into manufacturing chain level and workshop level at two levels to optimal scheduling.First,this paper analyses the conventional particle swarm optimization algorithm and its optimization mechanism,sums up the mainstream direction of particle swarm optimization and the characteristics of several improved particle swarm optimization.Then, compare the advantages and disadvantages of particle swarm optimization with genetic algorithm and ant colony algorithm.Secondly,this paper analyses the manufacturing chain collaborative scheduling problem and its model,analyzes the nature of the optimal solution,then puts forward the coding and decoding methods of the particles which corresponds to the question,and proposes the adaptive mutation binary particle swarm optimization algorithm which based on the traditional binary particle swarm optimization.During the running,the mutation probability for the current best particle is determined by two factors:the variance of the population fitness and average accumulative distance,such mutation can enhance the ability of the binary particle swarm optimization algorithm out of local optimal solution. Then use the adaptive mutation binary particle swarm optimization to solve manufacturing chain collaboration scheduling model,simulation results show the feasibility and effectiveness of the algorithm.Then,this paper researches the job shop scheduling problem,analyses the job shop scheduling problem and a suitable particle encoding and decoding method for it,and then use the adaptive particle swarm optimization algorithm with mutation operator to solve the job shop scheduling problem;and then analyses a flexible shop scheduling problem,and and then use the adaptive particle swarm optimization algorithm with mutation operator to solve the flexible job shop scheduling problem,simulation results show that this algorithm is effective and feasible for solving shop level scheduling problem.Finally,this paper sums up the research contents and results of the full text,and gives the research prospect for this article.