面向柔性作业调度问题的启发性规则改进遗传算法

摘要

对柔性作业调度问题,提出了一种启发性规则的改进遗传求解方法,此方法从启发性规则出发产生初始调度解.通过对初始调度解进行比较而产生初始种群.对初始种群通过启发规则的改进遗传算法进行优化计算,对染色体进行交叉、变异、交换和选择操作,应用启发式规则搜索关键工序并提高关键工序的交换、变异操作概率,在变异操作中利用启发式规则对变异过程加以引导,从而得到优化解.将此方法运用于一系列典型柔性调度问题进行了实验求解,并将求解结果与其他的计算方法进行了比较,表明此方法能提高求解效率,适合复杂的柔性作业调度问题求解.%The flexible job-shop scheduling problem (FJSP) is addressed and a hybrid genetic algorithm ( GA) is proposed to solve this problem. A heuristic is used to create a set of relatively good schedules for the problem, from which the initial population is created by selecting the relatively good ones. Furthermore, priority rules are integrated into crossover, mutation, exchange, and selection to avoid that it converges to a local optimum. In this way, a better solution can be obtained. Benchmark FJSPs are used to test the proposed method and comparison with some existing approaches is done. Results show that it is effective and efficient for solving FJSPs.