Batch loading and scheduling problem with processing time deterioration and rate-modifying activities
Jin Hyoung Kim, Jae Won Jang, David Kim, Byung Soo Kim
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.