Evolutionary algorithms for scheduling a flowshop manufacturing cell with sequence dependent family setups

Evolutionary algorithms for scheduling a flowshop manufacturing cell with sequence dependent family setups

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Article ID: iaor20052524
Country: Netherlands
Volume: 48
Issue: 3
Start Page Number: 491
End Page Number: 506
Publication Date: May 2005
Journal: Computers & Industrial Engineering
Authors: , , , ,
Keywords: heuristics
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.

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