A genetic algorithm for robust schedules in a just-in-time environment

Marc Sevaux and Kenneth Sörensen

Working Papers from University of Antwerp, Faculty of Business and Economics

Abstract: Computing a schedule for a single machine problem is often difficult for irregular criteria, but when the data are uncertain, the problem is much more complicated. In this paper, we modify a genetic algorithm to compute robust schedules when release dates are subject to small variations. Two types of robustness are distinguished: quality robustness or robustness in the objective function space and solution robustness or robustness in the solution space. We show that the modified genetic algorithm can find solutions that are robust with respect to both types of robustness. Moreover, the risk associated with a specific solution can be easily evaluated. The modified genetic algorithm is applied to a just-in-time scheduling problem, a common problem in many industries.

Keywords: Quality robustness; Solution robustness; Single machine scheduling problem; Weighted number of late jobs; Genetic algorithm (search for similar items in EconPapers)
Pages: 24 pages
Date: 2002-12
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Persistent link: https://EconPapers.repec.org/RePEc:ant:wpaper:2003008

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