Batch loading and scheduling problem with processing time deterioration and rate-modifying activities

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 vo...

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Bibliographic Details
Published in:International journal of production research 2022-03, Vol.60 (5), p.1600-1620
Main Authors: Kim, Yong Jae, Jang, Jae Won, Kim, David S., Kim, Byung Soo
Format: Article
Language:English
Subjects:
Algorithms
Batch processing
deteriorations
genetic algorithm
Genetic algorithms
Heuristic methods
Integer programming
Linear programming
Lower bounds
Mixed integer
mixed linear integer programming
rate-modifying activity
Scheduling
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Summary: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.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2020.1866783