Abstract: To solve group scheduling problem for flow shop in which setup time and handling time could be separated,a multi-objective decision model with the ideal point method which took the makespan as main optimization goal,and the shutdown frequency and the handling times of group jobs as subordinate optimization goals was established.Aiming at the problems of solving makespan and developing operational planning for group jobs,three types of time models were constructed.A new genetic algorithm of setup time and handling time separable was designed for group jobs scheduling.The effectiveness of proposed algorithm was verified through two small size simulation experiments.To further assess the performance of the algorithm for a large size problem,it was compared with the simple genetic algorithm,and the results showed that the researches could determine the optimal scheduling scheme of group jobs as well as make precise planning for the processes of processing and handling.The new designed genetic algorithm could get the ideal solution within an acceptable computation time.

Key words: flow shop group scheduling, multi-objective decision, setup time, handling time, genetic algorithms

摘要： 为解决调整时间与搬运时间可分离的流水车间成组调度问题,建立了以生产周期为主要目标、以停机次数和总搬运次数为次要目标的基于理想点法的多目标决策模型。针对成组零件生产周期求解和作业计划制定问题构建了三类时间模型。为有效对成组零件进行调度,设计了调整时间与搬运时间可分离的遗传算法。通过两个小规模仿真实验验证了该算法的有效性。为进一步评估该算法对较大规模算例的求解效果,将该算法与基本遗传算法进行了对比。研究结果表明：本研究可确定成组零件的最优排序方案,并能为工艺工序的加工、设备的调整以及运输工序的搬运制定精确的作业计划;同时,新设计的遗传算法在可接受的计算时间内能获得理想解。

关键词: 流水车间成组调度, 多目标决策, 调整时间, 搬运时间, 遗传算法