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|Type:||Artigo de evento|
|Title:||Evolutionary Algorithms For Scheduling A Flowshop Manufacturing Cell With Sequence Dependent Family Setups|
|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. © 2005 Elsevier Ltd. All rights reserved.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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