Metaheuristic Approaches for Scheduling Jobs on Parallel Batch Processing Machines

Stefan Lausch () and Lars Mönch ()
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Stefan Lausch: University of Hagen
Lars Mönch: University of Hagen

Chapter Chapter 10 in Heuristics, Metaheuristics and Approximate Methods in Planning and Scheduling, 2016, pp 187-207 from Springer

Abstract: Abstract We consider a scheduling problem for parallel identical batch processing machines. A batch is a set of jobs that can be processed at the same time on a single machine. The jobs belong to incompatible job families. Only jobs of the same family can be batched together. We are interested in minimizing the total weighted tardiness (TWT) of the jobs. Problems of this type arise, for instance, in semiconductor manufacturing. Other known occurrence of batch processing machines can be found in gear manufacturing. We describe a genetic algorithm (GA), an ant colony optimization (ACO) approach, and a large neighborhood search (LNS) approach for this scheduling problem. The performance of the three metaheuristic approaches is compared based on randomly generated problem instances. The LNS scheme outperforms the two other metaheuristics and is comparable with a variable neighborhood search (VNS) approach, the best performing heuristic for this scheduling problem from the literature.

Keywords: Parallel machines scheduling; Batch processing; Tardiness; Genetic algorithms; Ant colony optimization; Large neighborhood search (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-3-319-26024-2_10

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