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

Authors:
Paulo M. França;Jatinder N. D. Gupta;Alexandre S. Mendes;Pablo Moscato;Klaas J. Veltink
Affiliations:
Departamento de Engenharia de Sistemas, DENSIS Universidade Estadual de Campinas, UNICAMP C. P. 6101, 13081-970 Campinas, SP, Brazil;Department of Accounting and Information Systems, College of Administrative Science, The University of Alabama in Huntsville, Huntsville, AL;Department of Computer Science, School of Electrical Engineering and Computer Science, Faculty of Engineering and Built Environment, University of Newcastle Callaghan, 2308, NSW, Australia;Department of Computer Science, School of Electrical Engineering and Computer Science, Faculty of Engineering and Built Environment, University of Newcastle Callaghan, 2308, NSW, Australia;Department of Econometrics, University of Groningen Postbus 800, 9700 AV Groningen, The Netherlands
Venue:
Computers and Industrial Engineering - Special issue: Group technology/cellular manufacturing
Year:
2005

Citing 3
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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.