A General Model and Efficient Algorithms for Reliable Facility Location Problem Under Uncertain Disruptions

Yongzhen Li (), Xueping Li (), Jia Shu (), Miao Song () and Kaike Zhang ()
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Yongzhen Li: Department of Management Science and Engineering, School of Economics and Management, Southeast University, Nanjing, Jiangsu 210096, China
Xueping Li: Department of Industrial and Systems Engineering, The University of Tennessee at Knoxville, Knoxville, Tennessee 37996
Jia Shu: School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
Miao Song: Department of Logistics and Maritime Studies, Faculty of Business, The Hong Kong Polytechnic University, Hong Kong, China
Kaike Zhang: Department of Industrial and Systems Engineering, The University of Tennessee at Knoxville, Knoxville, Tennessee 37996

INFORMS Journal on Computing, 2022, vol. 34, issue 1, 407-426

Abstract: This paper studies the reliable uncapacitated facility location problem in which facilities are subject to uncertain disruptions. A two-stage distributionally robust model is formulated, which optimizes the facility location decisions so as to minimize the fixed facility location cost and the expected transportation cost of serving customers under the worst-case disruption distribution. The model is formulated in a general form, where the uncertain joint distribution of disruptions is partially characterized and is allowed to have any prespecified dependency structure. This model extends several related models in the literature, including the stochastic one with explicitly given disruption distribution and the robust one with moment information on disruptions. An efficient cutting plane algorithm is proposed to solve this model, where the separation problem is solved respectively by a polynomial-time algorithm in the stochastic case and by a column generation approach in the robust case. Extensive numerical study shows that the proposed cutting plane algorithm not only outperforms the best-known algorithm in the literature for the stochastic problem under independent disruptions but also efficiently solves the robust problem under correlated disruptions. The practical performance of the robust models is verified in a simulation based on historical typhoon data in China. The numerical results further indicate that the robust model with even a small amount of information on disruption correlation can mitigate the conservativeness and improve the location decision significantly. Summary of Contribution: In this paper, we study the reliable uncapacitated facility location problem under uncertain facility disruptions. The problem is formulated as a two-stage distributionally robust model, which generalizes several related models in the literature, including the stochastic one with explicitly given disruption distribution and the robust one with moment information on disruptions. To solve this generalized model, we propose a cutting plane algorithm, where the separation problem is solved respectively by a polynomial-time algorithm in the stochastic case and by a column generation approach in the robust case. The efficiency and effectiveness of the proposed algorithm are validated through extensive numerical experiments. We also conduct a data-driven simulation based on historical typhoon data in China to verify the practical performance of the proposed robust model. The numerical results further reveal insights into the value of information on disruption correlation in improving the robust location decisions.

Keywords: uncapacitated facility location; uncertain facility disruptions; stochastic and distributionally robust optimizations; cutting plane; column generation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:34:y:2022:i:1:p:407-426

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