A heterogeneous reliable location model with risk pooling under supply disruptions. (January 2016)

• Record Type: Journal Article
• Authors: Zhang, Ying; Snyder, Lawrence V.; Qi, Mingyao; Miao, Lixin
• Is Part Of: Transportation research. Volume 83(2016)
• Page Start: 151
• Page End: 178
• Publication Date: 2016-01
• Subjects: Facility location -- Disruption -- Risk pooling -- Concave minimization -- Lagrangian relaxation; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2015.11.009
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: We study a location problem that incorporates disruptions, the risk-pooling effect and economies of scale. We develop an exact and an approximate expression for the nonlinear inventory cost. We design an exact solution approach using SOS2 and a heuristic based on Lagrangian relaxation. Computational results show the effectiveness of our methods when compared with published results. Managerial insights on facility deployment, customer assignments and inventory control are drawn. Abstract: This paper investigates a facility location model that considers the disruptions of facilities and the cost savings from the inventory risk-pooling effect and economies of scale. Facilities may have heterogeneous disruption probabilities. When a facility fails, its customers may be reassigned to other surviving ones to hedge against lost-sales costs. We first develop both an exact and an approximate expression for the nonlinear inventory cost, and then formulate the problem as a nonlinear integer programming model. The objective is to minimize the expected total cost across all possible facility failure scenarios. To solve this problem, we design two methods, an exact approach using special ordered sets of type two (SOS2) and a heuristic based on Lagrangian relaxation. We test the model and algorithms on data sets with up to 150 nodes. Computational results show that the proposed algorithms can solve the problem efficiently in reasonable time. Managerial insights on the optimal facility deployment, customer assignments and inventory control strategies are also drawn.