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Local search heuristics for single machine scheduling with batching to minimize the number of late jobs

Author: Crauwels, H. A. J ; Potts, C. N. ; Van Wassenhove, Luk N.INSEAD Area: Technology and Operations ManagementIn: European Journal of Operational Research, vol. 90, no. 2, 1996 Language: EnglishDescription: p.200-213.Type of document: INSEAD ArticleNote: Please ask the Library for this articleAbstract: Local search heuristics are developed for a problem of scheduling jobs on a single machine. Jobs are partitioned into families, and set-up time is necessary when there is a switch in processing jobs from one family to jobs of another family. The objective is to minimize the number of late jobs. Four alternative local search method are proposed: multi-start descent, simulated annealing, tabu search and genetic algorithm. The performance of these heuristics is evaluated on a large set of test problems. The best results are obtained with the genetic algorithm; multi-start descent also performs quite well
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Local search heuristics are developed for a problem of scheduling jobs on a single machine. Jobs are partitioned into families, and set-up time is necessary when there is a switch in processing jobs from one family to jobs of another family. The objective is to minimize the number of late jobs. Four alternative local search method are proposed: multi-start descent, simulated annealing, tabu search and genetic algorithm. The performance of these heuristics is evaluated on a large set of test problems. The best results are obtained with the genetic algorithm; multi-start descent also performs quite well

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