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Ant colony optimization for solving an industrial layout problem

Abstract : This paper presents ACO_GLS, a hybrid ant colony optimization approach coupled with a guided local search, applied to a layout problem. ACO_GLS is applied to an industrial case, in a train maintenance facility of the French railway system (SNCF). Results show that an improvement of near 20% is achieved with respect to the actual layout. Since the problem is modeled as a quadratic assignment problem (QAP), we compared our approach with some of the best heuristics available for this problem. Experimental results show that ACO_GLS performs better for small instances, while its performance is still satisfactory for large instances.
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https://hal-utt.archives-ouvertes.fr/hal-02490985
Contributor : Daniel Gavrysiak <>
Submitted on : Tuesday, February 25, 2020 - 4:30:35 PM
Last modification on : Wednesday, February 26, 2020 - 2:02:54 AM

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Yasmina Hani, Lionel Amodeo, Farouk Yalaoui, Haoxun Chen. Ant colony optimization for solving an industrial layout problem. European Journal of Operational Research, Elsevier, 2007, 183 (2), pp.633-642. ⟨10.1016/j.ejor.2006.10.032⟩. ⟨hal-02490985⟩

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