Multiprocessor task scheduling in multistage hybrid flow-shops: a genetic algorithm approach

Multiprocessor task scheduling in multistage hybrid flow-shops: a genetic algorithm approach

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Article ID: iaor20051321
Country: United Kingdom
Volume: 55
Issue: 5
Start Page Number: 504
End Page Number: 512
Publication Date: May 2004
Journal: Journal of the Operational Research Society
Authors: ,
Keywords: production
Abstract:

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The objective is to minimize the makespan, that is, the completion time of all the tasks in the last stage. This problem is of practical interest in the textile and process industries. A genetic algorithm (GA) is developed to solve the problem. The GA is tested against a lower bound from the literature as well as against heuristic rules on a test bed comprising 400 problems with up to 100 jobs, 10 stages, and with up to five processors on each stage. For small problems, solutions found by the GA are compared to optimal solutions, which are obtained by total enumeration. For larger problems, optimum solutions are estimated by a statistical prediction technique. Computational results show that the GA is both effective and efficient for the current problem. Test problems are provided in a web site at www.benchmark.ibu.edu.tr/mpt-hfsp.

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