Title: An evolutionary programming approach for solving the capacitated facility location problem with risk pooling
Authors: A. Diabat, T. Aouam, L. Ozsen
Addresses: Engineering Systems and Management, Masdar Institute of Science and Technology, P.O. Box 54224, Abu Dhabi, UAE. ' Department of Industrial Engineering, AlHosn University, P.O. Box 38772, Abu Dhabi, UAE. ' Department of Decision Sciences, San Francisco State University, San Francisco, CA 94132, USA
Abstract: In this paper, we propose a genetic algorithm as an alternative technique for solving the capacitated facility location problem with risk pooling (CLMRP). The CLMRP is a joint location-inventory problem involving a single supplier and multiple retailers that face stochastic demand. Due to the stochasticity of demand associated with each retailer, risk pooling may be achieved by allowing some retailers to serve as distribution centres (DCs). This is a combinatorial optimisation problem that has been shown to be NP-hard. A genetic algorithm that is computationally very efficient is developed to solve the problem. A computational experiment is conducted to test the performance of the developed technique and computational results are reported. The algorithm can easily find optimal or near optimal solutions for benchmark test problems from the literature, where the Lagrangian relaxation approach was used.
Keywords: supply chain management; SCM; location-inventory problems; nonlinear integer programming; evolutionary programming; genetic algorithms; metaheuristics; capacitated facility location; risk pooling.
International Journal of Applied Decision Sciences, 2009 Vol.2 No.4, pp.389 - 405
Published online: 24 Jan 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article