Abstract: Conventional unrelated parallel machine scheduling typically ignores machines’ relia-bility loss during machine processing,resulting in production interruptions due to excessive ma-chine deterioration.Aiming at this problem,this paper constructs an integrated scheduling model which joint optimizes the scheduling scheme and preventive maintenance (PM) strategy by taking the jobs allocation and the machine working age threshold as the decision variables,and designs a hybrid discrete particle swarm algorithm (HDPSO) to solve it.HDPSO redefines the particle po-sition update mechanism in the solution space based on two-dimensional coding,incorporates a multi-neighborhood search strategy to balance the exploration and exploitation capabilities within the discrete solution space,and achieve the synergistic optimization of scheduling and PM in the maximum completion time index.Extensive simulation demonstrates that HDPSO has a high effectiveness in solving large scale unrelated parallel machine scheduling problems.Meanwhile,the proposed working age maintenance strategy is more conducive to increase productivity and scheduling performance than periodical maintenance.

Key words: preventive maintenance, unrelated parallel machine, working-age maintenance, dis-crete particle swarm optimization algorithm

摘要： 传统不相关并行机调度通常忽略机器可靠度在加工过程中的损耗,导致机器因劣化过度而带来生产中断。针对该问题,本文以工件分配与机器役龄阈值为决策变量,构建将调度方案与维护策略进行协同优化的集成调度模型,并设计一种混合离散粒子群算法对模型进行求解。该算法基于二维编码方式对粒子的位置更新机制进行重新定义,并融入多邻域搜索策略平衡算法在离散解空间内的探索与开发能力,实现调度与维护二者在最大完工时间指标上的协同优化。大量仿真结果验证了所提算法在求解大规模不相关并行机调度问题方面具有较强的搜索优势。同时,所提的役龄维护策略相较于定周期维护而言更有助于提升生产效率,改善调度性能。

关键词: 预防性维护, 不相关并行机, 役龄维护, 离散粒子群算法