改进的粒子群算法在多目标车间调度的应用

APPLICATION OF IMPROVED PARTICLE SWARM OPTIMIZATION IN MULTI-TARGET WORKING WORKSHOP SCHEDULING

利用粒子群算法解决车间调度问题,是一种有效的策略。对粒子群算法进行分析,针对多目标的柔性车间调度问题,构建了以加工时间最小化、加工成本最小化和单机器最大负荷最小化的多目标柔性车间调度模型。提出基于交叉变异的变参粒子群算法,以提高其跳出局部最优快速达到全局最优的能力。同时,引入智能小车概念,将运输时间考虑到此调度中。并将该方法用于某离散制造业的柔性车间作业调度中,最后验证了该算法的实用性及高效性。

Using particle swarm algorithm to solve the problem of workshop scheduling is a kind of effective strategy.In this paper,the particle swarm optimization algorithm was analyzed.Aiming at the multi-objective flexible shop scheduling problem,a multi-objective flexible shop scheduling model was constructed,which minimized the processing time,minimized the processing cost and minimized the maximum single machine load.A particle swarm optimization algorithm based on crossover mutation was proposed to improve its ability to jump out of local optimum and reach the global optimum.At the same time,the concept of smart car was introduced to consider the transportation time into this scheduling.The method was applied to the job shop scheduling of a flexible manufacturing plant in a discrete manufacturing industry.Finally,the practicability and efficiency of this algorithm were verified.