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Local search heuristics for single machine scheduling with batching to minimize the number of late jobs (RV of 95/28/TM)

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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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en

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application/pdf

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http://flora.insead.edu/fichiersti_wp/Inseadwp1995/95-73.pdf

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Copyright INSEAD. All rights reserved