School of Automation, Wuhan University of Technology
The National Natural Science Foundation of China (61573264,71471151)
针对具有预防性维修(PM)和顺序相关准备时间(SDST)的不相关并行机调度问题, 提出了一种多群体人工蜂群算法(MABC)以同时最小化完工时间和总延迟时间. 该算法将雇佣蜂分割成s个雇佣蜂群, 除最差雇佣蜂群外,每个雇佣蜂群都对应一个跟随蜂群. 结合两个目标函数、PM和SDST的特征设计了三种邻域搜索, 采用全局搜索和邻域搜索的不同组合实现雇佣蜂阶段和跟随蜂阶段, 并引入了两种淘汰过程. 通过大量实验测试MABC新策略与搜索性能, 计算结果验证了新策略的有效性和MABC的搜索优势.
To solve the unrelated parallel machine scheduling problem (UPMSP) with preventive maintenance (PM) and sequence dependent setup time (SDST), a multi-colony artificial bee colony (MABC) algorithm is proposed to minimize makespan and total tardiness simultaneously. In this algorithm, s employed bee colonies are obtained by division. Except for the worst employed bee colony, each employed bee colony corresponds to a onlooker bee colony. Combined with the characteristics of two objective functions, PM and SDST, three kinds of neighborhood searches were designed. Different combinations of global search and neighborhood search are used to implement the employed bee phase and the onlooker bee phase, and two elimination processes are applied. Experimental research on the new strategy and search performance of MABC is carried out, and computational results demonstrate the effectiveness of the new strategy and the search advantage of MABC.