多目标协同正弦优化算法求解分布式流水车间调度问题

Multi-objective collaborative sine optimization algorithm for the distributed flow-shop scheduling

中文关键词  多目标优化  分布式流水车间调度  序列相关准备时间  正弦优化算法  协同优化

英文关键词  multi-objective optimization  distributed flow-shop scheduling  sequence dependent setup time  sine optimization algorithm  collaborative optimization

中文摘要

      针对最小化最大完工时间(makespan)、总拖期以及平均空闲时间的多目标序列相关准备时间分布式流水车间调度问题, 本文提出一种多目标协同正弦优化算法(MCSOA). 算法主要包括4个核心阶段: 在多邻域搜索阶段,提出了基于关键工厂的搜索策略, 并通过正弦优化算法控制搜索范围; 在破坏重构阶段, 设计了一种迭代搜索策略引导个体的进化方向, 同时使用正弦优化算法平衡全局开发与局部搜索; 在选择阶段, 使用非支配排序与参考点的方法筛选优质解, 外部档案集用于存储所有非支配解; 在协同阶段, 设计种群间共享与竞争机制, 平衡3个目标的优化. 本文通过多目标优化的均匀性、反世代距离和覆盖率3项性能指标验证算法的有效性, 并使用非参数检验证明所提出的算法具有显著性优势.

英文摘要

      This study investigates the distributed flow shop scheduling problem with sequence-dependent setup time. A multi-objective collaborative sine optimization algorithm (MCSOA) is proposed to minimize the makespan, total tardiness and mean idle time of the problem. The MCSOA mainly contains four core phases. The sine optimization algorithm is used to control the search range of the multi-neighborhood search strategy which introduced key factories; in the destruction and reconstruction stage, an iterative search strategy is designed to guide the evolutionary direction of individuals, while the sine optimization algorithm is used to balance the global exploitation and the local search; in the selection phase, the fast non-dominated sorting and reference point methods are selected to screen high-quality solutions, and an archive set is designed to store all non-dominated solutions; in the collaborative phase, the sharing and competition scheme is designed to ensure that the three objectives can be optimized evenly. Performance indicators include spacing metric, inverse generation distance and coverage rate are used to verify the effectiveness of the algorithm. The non-parametric test shows significant advantages of the proposed algorithm.