ABSTRACT

This research addresses a single machine batch loading and scheduling problem. Jobs in the same family are processed as a batch in the machine with a known family-specific processing time. Each job in a batch requires a known volume or space, and the total batch volume cannot exceed the available volume/capacity of the machine. Batch processing times increase proportionately with the time since the most recent rate-modifying activity and the starting time of a batch. A rate-modifying activity can be executed which restores original batch processing times. In this research, a solution procedure is proposed that simultaneously determines the appropriate batching of jobs and the number of rate-modifying activities. Job batches and the rate-modifying activities are then sequenced to minimise the makespan. To develop a solution procedure, a mixed integer linear programming model is formulated and a tight lower bound is proposed. Three genetic algorithms (GAs), including batch loading and sequencing heuristics, are proposed. The performance of the three GAs is compared, and the best GA is compared to other meta-heuristic algorithms.

摘要

这项研究解决了单机批量加载和调度问题。同一系列中的作业在机器中以已知的家族特定处理时间批量处理。批次中的每个作业都需要已知的体积或空间，并且批次的总体积不能超过机器的可用体积/容量。自最近的速率修改活动和批次的开始时间以来，批次处理时间与时间成比例增加。可以执行速率修改活动，以恢复原始批处理时间。在这项研究中，提出了一种解决方法，可以同时确定适当的作业批次和调率活动的数量。然后对作业批次和调价活动进行排序，以最大程度地缩短工期。要制定解决程序，建立了混合整数线性规划模型，并提出了严格的下界。提出了三种遗传算法（GA），包括批量加载和排序启发式算法。比较了三个GA的性能，并将最佳的GA与其他元启发式算法进行了比较。