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Local search heuristic for single machine scheduling with batch set-up times

Author: Crauwels, H. A. J ; Potts, C. N. ; Van Wassenhove, Luk N.INSEAD Area: Technology and Operations Management Series: Working Paper ; 94/07/TM Publisher: Fontainebleau : INSEAD, 1994.Language: EnglishDescription: 13 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 batches, and a set-up time is necessary when there is a switch in processing jobs from one batch to jobs of another batch. The objective is to minimize the total weighted completion time. Three alternatives neighbourhood search methods are developed: multi-start descent, simulated annealing and tabu search. The performance of these heuristics is evaluated based on a large set of test problems, and the results are also compared with those obtained by a genetic algorithm
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Local search heuristics are developed for a problem of scheduling jobs on a single machine. Jobs are partitioned into batches, and a set-up time is necessary when there is a switch in processing jobs from one batch to jobs of another batch. The objective is to minimize the total weighted completion time. Three alternatives neighbourhood search methods are developed: multi-start descent, simulated annealing and tabu search. The performance of these heuristics is evaluated based on a large set of test problems, and the results are also compared with those obtained by a genetic algorithm

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