An Optimization-Based Heuristic for the Robotic Cell Problem

Jacques Carlier (1) , Mohamed Haouari (2, 3) , Mohamed Kharbeche (4) , Aziz Moukrim (1)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
2 Department of Industrial Engineering, Faculty of Engineering,
3 Princess Fatimah Alnijris’Research Chair for AMT, College of Engineering,
4 ROI - Unité Recherche Opérationnelle pour l'Industrie

This study investigates an optimization-based heuristic for the Robotic Cell Problem. This problem arises in automated cells and is a complex ‡ow shop problem with a single transportation robot and a blocking constraint. We propose an approximate decomposition algorithm. The proposed approach breaks the problem into two scheduling problems that are solved sequentially: a flow shop problem with additional constraints (blocking and transportation times) and a single machine problem with precedence constraints, time lags, and setup times. For each of these problems, we propose an exact branch-and-bound algorithm. Also, we describe a genetic algorithm that includes, as a mutation operator, a local search procedure. We report the results of a computational study that provides evidence that the proposed optimization-based approach delivers high-quality solutions and consistently outperforms the genetic algorithm. However, the genetic algorithm delivers reasonably good solutions while requiring significantly shorter CPU times.

Mots clés: Genetic Algorithm; Flow Shop; Robotic Cell; Blocking; Branch-and-bound; Genetic Algorithm.

Domaines: Recherche opérationnelle [math.OC]

Jacques Carlier, Mohamed Haouari, Mohamed Kharbeche, Aziz Moukrim. An Optimization-Based Heuristic for the Robotic Cell Problem. European Journal of Operational Research, 2010, 202 (3), pp.636-645. ⟨10.1016/j.ejor.2009.06.035⟩. ⟨hal-00452706⟩