多目标柔性车间调度的文化基因非支配排序粒子群算法

Memetic Non-dominated Sorting Particle Swarm Optimization Algorithm for Solving the Multi-objective Flexible Job Shop Scheduling Problem

本文以离散型柔性制造车间为对象,以缩短生产周期、减少机器空转时间和提高产品合格率为优化目标,提出一种文化基因非支配排序粒子群算法.该算法采用二维编码方式.首先,分别对工序和机器分配进行不同的变异操作,建立了多目标离散型资源优化调度模型.然后,采用非支配排序策略和随机游走法获得Pareto最优解,接着利用层次分析法给出资源优化配置方案.最后,利用实际生产数据进行仿真,结果表明所提出的优化算法具有平衡全局搜索能力和局部搜索能力的特性.

In this paper, a memetic non-dominated sorting particle swarm optimization algorithm is proposed for the discrete flexible job shop scheduling. Shorting the production period, reducing the machine idle time and improving the product qualification rate are the algorithm＇s optimization objectives. This algorithm adopts two-dimensional coding method. First, a multi-objective discrete resources optimization scheduling model is established by different mutation operation for the process and the machine allocation. Then, the Pareto optimal solution is obtained using the non-dominated sorting strategy and the random walk method. Besides, using the analytic hierarchy method, the resource optimal allocation scheme is given. Finally, the actual production data is used for simulation. The result shows that the proposed optimization algorithm can balance the global search and the local exploitation abilities.