Resource title

Local search heuristic for single machine scheduling with batching to minimize total weighted completion time

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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 total weighted completion time. Four alternative neighbourhood search methods are developed: multi-start descent, simulated annealing, threshold accepting and tabu search. The performance of these heuristics is evaluated on a large set of test problems, and the results are also compared with those obtained by a genetic algorithm. The tabu search method generates high quality schedules relative to the other methods at modest computational expense

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en

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application/pdf

Resource resource URL

http://flora.insead.edu/fichiersti_wp/Inseadwp1995/95-28.pdf

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Copyright INSEAD. All rights reserved