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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 Management Series: Working Paper ; 94/45/TM Publisher: Fontainebleau : INSEAD, 1994.Language: EnglishDescription: 15 p.Type of document: INSEAD Working Paper Online Access: Click here Abstract: Local search heuristics are developed for a problem of scheduling jobs on a single machine. Jobs are partitioned into families, and a 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 methods are developed: multi-start descent, simulated annealing, tabu search and a genetic algorithm. The performance of these heuristics is evaluated on a large set of test problems. The best results are obtained by 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 a 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 methods are developed: multi-start descent, simulated annealing, tabu search and a genetic algorithm. The performance of these heuristics is evaluated on a large set of test problems. The best results are obtained by the genetic algorithm; multi-start descent also performs quite well

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