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Genetic algorithms in integrated process planning and scheduling

Genetic algorithms in integrated process planning and scheduling Process planning and scheduling are actually interrelated and should be solved simultaneously. Most integrated process planning and scheduling methods only consider the time aspects of the alternative machines when constructing schedules. The initial part of this paper describes a genetic algorithm (GA) based algorithm that only considers the time aspect of the alternative machines. The scope of consideration is then further extended to include the processing capabilities of alternative machines, with different tolerance limits and processing costs. In the proposed method based on GAs, the processing capabilities of the machines, including processing costs as well as number of rejects produced in alternative machine are considered simultaneously with the scheduling of jobs. The formulation is based on multi-objective weighted-sums optimization, which are to minimize makespan, to minimize total rejects produced and to minimize the total cost of production. A comparison is done w ith the traditional sequential method and the multi-objective genetic algorithm (MOGA) approach, based on the Pareto optimal concept. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Intelligent Manufacturing Springer Journals

Genetic algorithms in integrated process planning and scheduling

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References (29)

Publisher
Springer Journals
Copyright
Copyright © 1999 by Kluwer Academic Publishers
Subject
Business and Management; Production; Manufacturing, Machines, Tools; Control, Robotics, Mechatronics
ISSN
0956-5515
eISSN
1572-8145
DOI
10.1023/A:1008976720878
Publisher site
See Article on Publisher Site

Abstract

Process planning and scheduling are actually interrelated and should be solved simultaneously. Most integrated process planning and scheduling methods only consider the time aspects of the alternative machines when constructing schedules. The initial part of this paper describes a genetic algorithm (GA) based algorithm that only considers the time aspect of the alternative machines. The scope of consideration is then further extended to include the processing capabilities of alternative machines, with different tolerance limits and processing costs. In the proposed method based on GAs, the processing capabilities of the machines, including processing costs as well as number of rejects produced in alternative machine are considered simultaneously with the scheduling of jobs. The formulation is based on multi-objective weighted-sums optimization, which are to minimize makespan, to minimize total rejects produced and to minimize the total cost of production. A comparison is done w ith the traditional sequential method and the multi-objective genetic algorithm (MOGA) approach, based on the Pareto optimal concept.

Journal

Journal of Intelligent ManufacturingSpringer Journals

Published: Sep 29, 2004

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