Ant colony optimization for solving an industrial layout problem

Yasmine Hani (1) , Lionel Amodeo (1) , Farouk Yalaoui (1) , Haoxun Chen (1)

1 LOSI - Laboratoire d'Optimisation des Systèmes Industriels

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 workshop 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.

Mots clés: guided local search.; ant colony optimization; guided local search; layout problem; quadratic assignment problem

Domaines: Automatique / Robotique

Yasmine Hani, Lionel Amodeo, Farouk Yalaoui, Haoxun Chen. Ant colony optimization for solving an industrial layout problem. European Journal of Operational Research, 2007, 2 (183), pp.633-642. ⟨10.1016/j.ejor.2006.10.032⟩. ⟨hal-00661830⟩