Abstract: Traditional flexible job shop scheduling usually ignores the transportation time and energy consumption of workpieces between machines.To solve this problem,a flexible job shop scheduling model by considering transportation constraints and energy saving was established,and an improved fast elitist Non-dominated Sorting Genetic Algorithm(NSGA-II)was proposed to solve this model.Four goals of maximum completion time,total delay,total equipment load,and total energy consumption of the workshop were established in the mathematical model of flexible job shop scheduling,and the scheduling model matrix encoding,decoding,crossover and mutation were realized according to transportation constraints.It is easy to fall into the problem of local optimal solution in the iterative process of improving NSGA-II algorithm by learning from the optimal solution mechanism.Under the premise of considering the transportation constraints between machines in the workshop,the feasibility analysis of the scheduling model was carried out in combination with the Kacem and Brandimarte examples.The results showed that the model and algorithm were highly efficient and could effectively solve the scheduling scheme and actual processing deviation caused by the workshop transportation constraints.

Key words: flow shop scheduling, transportation constraints, improved fast elitist non-dominated sorting genetic algorithm, workshop scheduling examples

摘要： 传统柔性作业车间调度通常忽略工件在机器间的运输时间和能耗,针对该问题建立了考虑运输约束与节能的柔性作业车间调度模型,并提出了改进的NSGA-Ⅱ算法求解该模型。首先,在柔性作业车间调度数学模型中设立最大完工时间、总延期、设备总负载、车间总能耗4个目标,并根据运输约束实现了调度模型矩阵编码、解码、交叉与变异,基于子代向最优解学习机制改进NSGA-Ⅱ算法迭代过程中易陷入局部最优解问题。最后,在考虑车间机器之间运输约束的前提下结合Kacem、Brandimarte算例对调度模型进行可行性分析,结果表明该模型与算法求解效率高,能有效解决车间运输约束导致的调度方案与实际加工偏差问题。

关键词: 柔性作业车间调度, 运输约束, 改进NSGA-II算法, 车间调度算例