基于遗传算法的多目标柔性工作车间调度问题求解

本文针对柔性工作车间调度问题给出了一个有意义的综合目标尽可能缩短制造周期的同时尽可能的减少机器负荷。由于传统遗传算法在多目标柔性工作车间调度问题上的局限性，我们提出了一种改进遗传算法：首先，我们给出了针对综合目标的工序调度算法获得初始集合；接着，针对柔性工作车间调度问题的特点，我们在常用的基于工序顺序的编码方法上融入了基于机器分配的编码方法，并据此设计了相应的交叉变异操作；最后借鉴了物种进化现象中的环境迁移思想设计了解决多目标优化问题的迁移操作。实验结果表明，改进的遗传算法在多目标柔性工作车间调度问题的解决上要优于传统遗传算法。

The Solution For Multi-Objective Flexible Job Shop Scheduling Based on Genetic Algorithm

This paper provides a meaningful comprehensive goal for the flexible job shop scheduling-Make the possible reduction of the machine burden while we shorten the span of manufacturing. Because the traditional genetic algorithm has localizations in the solution to flexible job shop scheduling, we propose an improved genetic algorithm. Firstly, we give the algorithm of scheduling according to the compositive objective in order to obtain initial solutions. Secondly, we add the coding method based on machine assignment to the general coding method based on procedure order and design the corresponding crossover, mutation operations according to the characteristic of flexible job shop scheduling. Finally, we design the migration operation to solve multi-objective optimization problems according to the idea of environment migration in the phenomenon of species evolution. The result of experiment shows than the improved genetic algorithm is superior to the traditional one in the solution to multi-objective flexible job shop scheduling.