Unified framework for choice-based facility location problem

Hui Lin, Yun; Tian, Qingyun; Zhao, Yanlu

Abstract

The choice-based facility location (CBFL) problem arises in various industrial and business contexts. The problem stands on a decentralized perspective: Companies set up chains of facilities, and customers determine from which chain or facility to seek service according to their own preferences. Essentially, customer preferences or choices play a key role in characterizing various CBFL problems, which differ mainly in the models or rules used to characterize the choice. Consequently, a large number of formulations appear and are often solved by dedicatedly designed approaches in the literature. Such a situation significantly complicates practitioners’ decision-making process when they are facing practical problems but are unsure which ad hoc model is suitable for their cases. In this article, we address this dilemma by providing a unified modeling framework based on the concept of preference dominance. Specifically, we conceptualize the choice behavior as a sequential two-step procedure: Given a set of open facilities, each customer first forms a nondominated consideration set and then splits the buying power within the set. Such an interpretation renders practitioners high modeling flexibility as they can tailor how preference dominance is constructed according to their specific contexts. In particular, we show that our model can represent several streams of CBFL problems. To support our model’s applicability, we design an efficient exact decomposition algorithm. Extensive computational studies reveal that although the algorithm is designed for a general purpose, it outperforms most approaches that are tailored for ad hoc problems by a large margin, which justifies both the effectiveness and the efficiency of the unified framework.

Citation

Hui Lin, Y., Tian, Q., & Zhao, Y. (2024). Unified framework for choice-based facility location problem. INFORMS Journal on Computing, https://doi.org/10.1287/ijoc.2022.0366

• Journal Article Type: Article
• Acceptance Date: Jan 9, 2024
• Online Publication Date: Mar 1, 2024
• Publication Date: Mar 1, 2024
• Deposit Date: Jan 10, 2024
• Publicly Available Date: Mar 15, 2024
• Journal: INFORMS Journal on Computing
• Print ISSN: 1091-9856
• Electronic ISSN: 1526-5528
• Publisher: Institute for Operations Research and Management Sciences
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1287/ijoc.2022.0366
• Public URL: https://durham-repository.worktribe.com/output/2117686