Multi-Objective Metaheuristics for the Joint Scheduling of Production and Maintenance

Ali Berrichi (1) , Farouk Yalaoui (2)

1 LIMOSE - Laboratoire d’Informatique de Modélisation d’Optimisation et de Systèmes Electroniques [Boumerdes]
2 LOSI - Laboratoire d'Optimisation des Systèmes Industriels

This chapter elaborates upon some hybrid multi‐objective particle swarm optimization (MOPSO) algorithms for the generation of Pareto solutions. A section outlines the state of the art on the joint production‐maintenance problem. Another section illustrates an integrated bi‐objective model. Two other sections present an overview of the working mechanisms of PSO and some of its applications, and discuss some hybrid multi‐objective algorithms based on PSO for the model. A comparative experimental study with two well‐known multi‐objective genetic algorithms (MOGAs) (strength Pareto evolutionary algorithm (SPEA 2) and nondominated sorting genetic algorithm (NSGA‐II)) is carried out in the penultimate section. The concluding section presents some research perspectives.

Mots clés: joint production‐maintenance problem; multi‐objective particle swarm optimization (MOPSO) algorithms; nondominated sorting genetic algorithm (NSGA‐II); strength Pareto evolutionary algorithm (SPEA 2)

Domaines: Recherche opérationnelle [math.OC]; Algorithme et structure de données [cs.DS]

Ali Berrichi, Farouk Yalaoui. Multi-Objective Metaheuristics for the Joint Scheduling of Production and Maintenance. Metaheuristics for Production Scheduling, John Wiley & Sons, Inc., pp.283-313, 2013, ⟨10.1002/9781118731598.ch11⟩. ⟨hal-02499361⟩