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Combinatorial Optimization of Stochastic Multi-objective Problems: an Application to the Flow-shop Scheduling Problem

Abstract : The importance of multi-objective optimization is globably stablished nowadays. Furthermore, a great part of real-world problems are subject to uncertainties due to, e.g., noisy or approximated fitness function(s), varying parameters or dynamic environments. Moreover, although evolutionary algorithms are commonly used to solve multi-objective problems on the one hand and to solve stochastic problems on the other hand, very few approaches combine simultaneously these two aspects. Thus, flow-shop scheduling problems are generally studied in a single-objective deterministic way whereas they are, by nature, multi-objective and are subjected to a wide range of uncertainties. However, these two features have never been investigated at the same time. In this paper, we present and adopt a proactive stochastic approach where processing times are represented by random variables. Then, we propose several multi-objective methods that are able to handle any type of probability distribution. Finally, we experiment these methods on a stochastic bi-objective flow-shop problem.
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https://hal.inria.fr/inria-00269974
Contributor : Arnaud Liefooghe <>
Submitted on : Thursday, April 3, 2008 - 1:43:01 PM
Last modification on : Thursday, May 28, 2020 - 9:22:09 AM
Document(s) archivé(s) le : Thursday, May 20, 2010 - 10:54:47 PM

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Arnaud Liefooghe, Matthieu Basseur, Laetitia Jourdan, El-Ghazali Talbi. Combinatorial Optimization of Stochastic Multi-objective Problems: an Application to the Flow-shop Scheduling Problem. Evolutionary Multi-criterion Optimization (EMO 2007), Feb 2007, Matsushima, Japan. pp.457--471, ⟨10.1007/978-3-540-70928-2_36⟩. ⟨inria-00269974⟩

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