期刊
COMPUTERS & INDUSTRIAL ENGINEERING
卷 48, 期 3, 页码 491-506出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2003.11.004
关键词
flowshop scheduling; family sequence dependent setups; manufacturing cells; group technology; evolutionary algorithms; empirical results
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. (c) 2005 Elsevier Ltd. All rights reserved.
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