Multi-product sequencing and lot-sizing under uncertainties: A memetic algorithm

Kseniya Schemeleva 1, 2 Xavier Delorme 3 Alexandre Dolgui 1, 2 Frédéric Grimaud 1, 2
1 LIMOS - Laboratoire d'Informatique, de Modélisation et d'optimisation des Systèmes
2 DEMO-ENSMSE - Département Décision en Entreprise : Modélisation, Optimisation
3 Sciences et Technologies de l'Information
LIMOS - Laboratoire d'Informatique, de Modélisation et d'optimisation des Systèmes, DEMO-ENSMSE - Département Décision en Entreprise : Modélisation, Optimisation
Abstract : The paper deals with a stochastic multi-product sequencing and lot-sizing problem for a line that produces items in lots. Two types of uncertainties are considered: random lead time induced by machine breakdowns and random yield to take into account part rejects. In addition, sequence dependent setup times are also included. This study focuses on maximizing the probability of producing a required quantity of items of each type for a given finite planning horizon. A decomposition approach is used to separate sequencing and lot-sizing algorithms. Previous works have shown that the sequencing sub-problem can be solved efficiently, but the lot-sizing sub-problem remains difficult. In this paper, a memetic algorithm is proposed for this second sub-problem. Computational results show that the algorithms developed can be efficiently used for large scale industrial instances.
Keywords : Lot-sizing Sequencing Random lead time Random yield Optimization Memetic algorithm
Type de document :
Article dans une revue
Engineering Applications of Artificial Intelligence, Elsevier, 2012, Volume 25 (Issue 8), pp.Pages 1598-1610. 〈10.1016/j.engappai.2012.06.012〉
Liste complète des métadonnées
https://hal-emse.ccsd.cnrs.fr/emse-00735413
Contributeur : Florent Breuil <>
Soumis le : mardi 25 septembre 2012 - 16:34:59
Dernière modification le : dimanche 28 janvier 2018 - 15:22:05

Identifiants

Collections

EMSE | DEMO-ENSMSE | FAYOL-ENSMSE | LIMOS | PRES_CLERMONT | TDS-MACS | SIGMA-CLERMONT

Citation

Kseniya Schemeleva, Xavier Delorme, Alexandre Dolgui, Frédéric Grimaud. Multi-product sequencing and lot-sizing under uncertainties: A memetic algorithm. Engineering Applications of Artificial Intelligence, Elsevier, 2012, Volume 25 (Issue 8), pp.Pages 1598-1610. 〈10.1016/j.engappai.2012.06.012〉. 〈emse-00735413〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

171

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD