The Use of Metaheuristics for a Stochastic Supply Chain Design Problem’s Resolution –A Comparison Study–

Document Type: ICIE 2016

Authors

1 Manufacturing Engineering Laboratory of Tlemcen, Tlemcen, Algeria

2 Laboratory of Industrial Engineering of Production and Maintenance, Metz, France

Abstract

In a competitive and maintainability context, each company finds to optimize her supply chain in order to maintain her customers by providing the best quality of products in the best delays and with the lost costs. In this sense, we are interested to a single commodity stochastic supply chain design problem. Our supply chain is composed of suppliers and retailers; the objective is to find the best location of distribution centres (DCs) and to serve retailers from suppliers trough DCs in a random supply lead time. We presented a non-linear optimization model integrated selection of suppliers, the location of DCs, and retailers allocation decisions with an oriented cost function to minimize. Note that the determination of exact solutions for this problem is a NP-hard problem. Accordingly, we propose an optimization approach using three different metaheuristics: genetic algorithm, simulated annealing and taboo search to solve this problem in order to find the best supply chain structure (location of DCs, allocation of suppliers to DCs and DCs to retailers). Computational results are presented and compared to evaluate the efficiency of the proposed approaches.

Keywords

Main Subjects


Benyoucef, L., Xie X., and Tanonkou G.A. (2013). Supply chain network design with unreliable suppliers: a Lagrangian relaxation-based approach. International Journal of Production Research, Vol. 51 (21), pp. 6435-6454.

Bischoff M. and Kerstin D. (2009). Allocation search methods for a generalized class of location–allocation problems. European Journal of Operational Research, Vol. 192, pp. 793-807.

Daskin M.S., Coullard C. and Shen, Z.J.M. (2006). An inventory-Location Model: Formulation, Solution Algorithms and Computational results. Annals of Operations Research, Vol. 110, pp. 83-106.

De Boer L., Labro E. and Morlacchi P. (2001). A review of methods supporting supplier selection. European Journal of Purchasing and Supply Management, Vol. 7, pp. 75-89.

Felfel H., Ayadi O. and Masmoudi F. (2015). A stochastic programming approach for a multi-site supply chain planning in textile and apparel industry under demand uncertainty. International Journal of Supply and Operations Management, Vol. 2 (3), pp. 925-946.

Gebennini E., Gamberini R. and Manzini R. (2009). An integrated production–distribution model for the dynamic location and allocation problem with safety stock optimization. The international journal of production and economics, Vol. 122, pp. 286-304.

Inemek A. and Tuna O. (2009). Global supplier selection strategies and implications for supplier performance: Turkish suppliers' perception. International Journal of Logistics Research and Applications, Vol. 12, pp. 381-406.

Jain V., Benyoucef L. and Deshmukh S.G. (2009). Strategic supplier selection: some emerging issues and challenges. Int. J. Logistics Systems and Management, Vol. 5 (1/2), 61-88.

Maliki F., Benyoucef L. and Sari Z. (2011). Sensitivity analysis for a stochastic multi modal location-allocation integrated suppliers selection problem. The international conference on Industrial Engineering and System Management (IESM 2011), Metz, France.

Maliki F. and Sari Z. (2012). Etude comparative des politiques de gestion de stock lors de la conception des chaînes logistiques. 9e conférence internationale de Modélisation, Optimisation et Simulation (MOSIM’12), Bordeaux, France.

Maliki F., Sari Z. and Souier M. (2013). Resolution of stochastic supply chain design problem by metaheuristic. The international conference Control, Decision and Information Technologies (CODIT’13), proceedings, Hammamet, Tunisia, pp. 366-371.

Maliki F., Brahami M.A., Dahane M. and Sari Z. (2014). A location-allocation problem design with unvailabilities management. 44th international conference on computers & industrial engineering (CIE44&IMSS’14) proceedings, Istanbul, Turkey, pp. 669-679.

Maliki F., Brahami M.A., Dahane M. and Sari Z. (2016). Facility unvailabilities management and supply chains design. Journal européen des systèmes automatisés, Vol. 49 (4-5), pp. 471-485.

Melo M.T., Nickel S. and Saldanha-da-gama F. (2009). Facility location and supply chain management – A review. European Journal of Operational Research, Vol. 196(2), pp. 401-412.

Owen S. H. and Daskin M.S. (1998). Strategic facility location: A review. European Journal of Operational Research, Vol. 111 (3), pp. 423-447.

Rezaei A. H. and Adressi A. (2015). Supply chain performance evaluation using data envelopment analysis. International Journal of Supply and Operations Management, Vol. 2 (2), pp. 748-758.

Shen Z.J.M., Coullard C. and Daskin M.S. (2003). A Joint Location-Inventory Model. Transportation Science, 37(1), pp. 40-55.

Shishebori D. and Ghaderi A. (2015). An integrated approach for reliable facility location/network design problem with link disruption. International Journal of Supply and Operations Management, Vol. 2 (1), pp. 640-661.

Snyder L.V. and Daskin M.S. (2005). Reliability Models for Facility Location: the expected failure cost case. Transportation Science, Vol. 39 (3), pp. 400-416.

Tanonkou G.A., Benyoucef L. and Xie X. (2006). A Two-Period Stochastic programming Model for Distribution network Design. Proceedings of the 12th IFAC Symposium on Information Control Problems in Manufacturing, St Etienne, France, pp. 377-382.

Tanonkou G.A., Benyoucef L. and Xie X. (2007). Joint Facility Location and Supplier Selection Decisions of distribution Networks with Random Supply Lead Time. International Conference on Industrial Engineering and Systems Management (IESM 2007), Pékin, Chine, pp. 1-10.

Tanonkou G.A. (2007). Une approche par relaxation lagrangienne pour l’optimisation d’un réseau de distribution : modèles stochastiques et fiables, université Paul Verlaine de Metz.

Vijayashree M., and Uthayakumar R. (2015). Integrated inventory model with controllable lead time involving investment for quality improvement in supply chain system. International Journal of Supply and Operations Management, Vol. 2 (1), pp. 617-639.