求解模糊柔性作业车间调度的多目标进化算法

摘要

针对实际制造车间中工序加工时间具有不确定性,将加工时间采用模糊数表示,建立一种多目标模糊柔性作业车间调度模型,并提出了有效求解该模型的多目标进化算法.算法采用混合机器分配和工序排序策略的方法产生初始种群,并采用插入空隙法对染色体进行解码.定义一种新的基于可能度的个体支配关系和一种基于决策空间的拥挤算子,并将所提支配关系和拥挤算子运用于快速非支配排序.接着,提出一种基于移动模糊关键工序的局部搜索策略.实验部分首先通过田口试验方法来研究关键参数对算法性能的影响;其次,将所提算法与三种不同的优化算法作对比.实验结果验证了所提算法的有效性.%Seeing that the processing time is uncertain in the actual manufacturing workshop,a multi-objective fuzzy flexible job shop scheduling model is established,and then an effective multi-objective evolutionary algorithm (MOEA) is proposed to solve this model.First,a method of mixing different machine allocation and operation sequencing strategies is adopted to generate initial population and a well-designed greedy inserting algorithm is adopted for chromosome decoding.Second,a Pareto dominant relation based on possibility degree and a modified crowding distance measure in decision space are defined and further employed to improve the fast nondominated sorting.Moreover,a problem-specific local search based on fuzzy critical path theory is incorporated into MOEA.Afterwards,the influence of key parameters is investigated based on the Taguchi method of experiment.Finally,extensive comparison with three existing algorithms is carried out,and the results demonstrate the effectiveness of the proposed algorithm.