研究了考虑机器恶化和工人学习效应的平行机连续 批调度问题, 其中, 工件具有不同的一般加工时间, 机器具有不同 的恶化率, 工人具有不同的学习能力, 批次的容量对于所有机器是相同的.目标是最小化最大完工时间.论文首先针对工件的组批排序问题推导了一系列重要性质, 并提出了相应的启发式组批策略.然后, 基于给定的工件分配和每个机器上工件的组批和排序, 研究设计了工人和机器启发式匹配策略.由于所研究的问题在一般情形下被证明是NP-hard问题, 论文设计了改进的变邻域搜索算法(IVNS)求解该问题并用算例验证了所提出算法的有效性.

This paper considers a parallel machine serial-batching scheduling problem considering deteriorating effect and learning effect, where jobs have different normal processing times, machines are of different deteriorating rates, and operators have different learning rates. The objective is to minimize the makespan. Firstly, a series of structural properties on jobs batching and sequencing are proposed, based on which a heuristic algorithm is developed. Then, for the given jobs assignment, batching, and sequencing, a heuristic is proposed to assign the operators to the machines. Since the problem is proved to be NP-hard in general case, an improved variable neighborhood search algorithm (IVNS) is designed to solve the problem and the computational results are used to validate the performance of the proposed methods.