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A genetic algorithm methodology for complex scheduling problems

✍ Scribed by Bryan A. Norman; James C. Bean

Publisher: John Wiley and Sons
Year: 1999
Tongue: English
Weight: 79 KB
Volume: 46
Category: Article
ISSN: 0894-069X
DOI: 10.1002/(sici)1520-6750(199903)46:2<199::aid-nav5>3.0.co;2-l

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✦ Synopsis

This paper considers the scheduling problem to minimize total tardiness given multiple machines, ready times, sequence dependent setups, machine downtime and scarce tools. We develop a genetic algorithm based on random keys representation, elitist reproduction, Bernoulli crossover and immigration type mutation. Convergence of the algorithm is proved. We present computational results on data sets from the auto industry. To demonstrate robustness of the approach, problems from the literature of different structure are solved by essentially the same algorithm.

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