An effective particle swarm optimization algorithm for flexible job-shop scheduling problem

Maroua Nouiri (1) , Abderezak Jemai (1) , Ahmed Chiheb Ammari (1, 2) , Abdelghani Bekrar (3) , Smail Niar (3)

1 INSAT - Institut National des Sciences Appliquées et de Technologie [Tunis]
2 King Abdulaziz University
3 LAMIH - Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201

Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, we apply particle swarm optimization (PSO) algorithm to solve this FJSP problem aiming to minimize the maximum completion time criterion. Various benchmark data taken from literature, varying from Partiel FJSP and Total FJSP, are tested. Computational results proved that the PSO developed is enough effective and efficient to solve flexible job-shop scheduling problem.

Mots clés: Genetic algorithms; Job shop scheduling; Sociology; Statistics; Topology; Particle swarm optimization

Domaines: Automatique / Robotique

Maroua Nouiri, Abderezak Jemai, Ahmed Chiheb Ammari, Abdelghani Bekrar, Smail Niar. An effective particle swarm optimization algorithm for flexible job-shop scheduling problem. IEEE International Conference on Industrial Engineering and Systems Management IESM’13, Institute of Electrical and Electronics Engineers (IEEE), Oct 2013, Rabat, Morocco. pp.29-34. ⟨hal-03450910⟩