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A heterogeneous reliable location model with risk pooling under supply disruptions
Institution:1. Department of Industrial Engineering, Tsinghua University, Beijing 100084, China;2. Research Center on Modern Logistics, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China;3. Department of Industrial and Systems Engineering, Lehigh University, 200 West Packer Ave., Mohler Lab, Bethlehem, PA 18015, USA;1. Department of Industrial Engineering, Tsinghua University, Beijing 100084, China;2. Department of Civil and Environmental Engineering, Portland State University, PO Box 751 - CEE, Portland, OR 97207, USA;1. Ecole des Mines de Saint-Etienne, Department of Manufacturing Sciences and Logistics, CMP, CNRS UMR 6158 LIMOS, 880 avenue de Mimet, 13541 Gardanne, France;2. School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran;3. Beedie School of Business, Simon Fraser University, Vancouver, Canada
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.
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