考虑运输时间的多目标模糊柔性作业车间节能调度研究

Optimizing Energy for Multi-Objective Scheduling of Fuzzy Flexible Job Shop Considering Transit Time

为降低柔性作业车间调度中的能耗,针对实际生产工序中加工时间的不确定性,建立以完工时间、生产成本、能源消耗为优化目标的多目标节能调度模型,并提出一种改进的人工蜂群算法.利用阈值策略动态调整交叉变异的顺序,以增强算法的搜索性能;利用基于模糊关键链以及工序序列的变邻域搜索机制来增强算法的局部搜索能力;设计新型拥挤距离并引入基于可能度的个体支配关系用于快速非支配排序,来筛选优良的个体;利用基于加权模糊目标函数值的锦标赛机制来有效筛选参与邻域搜索的个体.最后,通过基准案例进行测试并与其他算法的结果进行对比,验证了改进人工蜂群算法对于解决模糊柔性作业车间调度问题的有效性.

In order to reduce the energy consumption in fexible job shop scheduling,in view of the uncertainty of the processing time in the actual production workshop procedures,a multi-objective energy-saving scheduling model with the optimization goals of completion time,production cost,and energy consumption was established,and an improved artificial bee algorithm was proposed.In order to enhance the search performance of the algorithm,threshold strategy was used to dynamically adjust the order of crossover and mutation;variable neighborhood search mechanism based on fuzzy critical chain and process sequence was designed to enhance the local search ability of the algorithm;a new crowding distance was used and individual support relation based on possibility degree was introduced to fast non dominated sorting to screen excellent individuals;weighted fuzzy objective was used to optimize the search performance of the algorithm.The tournament mechanism of scalar function value was used to effectively screen the individuals participating in the neighborhood search.Finally,the benchmark case was tested and compared with the results of other algorithms to verify the effectiveness of the improved artificial bee colony algorithm for solving the fuzzy flexible job shop scheduling problem.