Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10523290 | Computers & Industrial Engineering | 2005 | 16 Pages |
Abstract
This paper considers the problem of scheduling part families and jobs within each part family in a flowshop manufacturing cell with sequence dependent family setups times where it is desired to minimize the makespan while processing parts (jobs) in each family together. Two evolutionary algorithms-a Genetic Algorithm and a Memetic Algorithm with local search-are proposed and empirically evaluated as to their effectiveness in finding optimal permutation schedules. The proposed algorithms use a compact representation for the solution and a hierarchically structured population where the number of possible neighborhoods is limited by dividing the population into clusters. In comparison to a Multi-Start procedure, solutions obtained by the proposed evolutionary algorithms were very close to the lower bounds for all problem instances. Moreover, the comparison against the previous best algorithm, a heuristic named CMD, indicated a considerable performance improvement.
Keywords
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Physical Sciences and Engineering
Engineering
Industrial and Manufacturing Engineering
Authors
Paulo M. França, Jatinder N.D. Gupta, Alexandre S. Mendes, Pablo Moscato, Klaas J. Veltink,