Code and Data Repository for Unified Framework for Choice-Based Facility Location Problem
针对多种基于顾客选择的设施选址问题，提出了一个基于偏好优势的统一建模框架，并设计了高效精确分解算法，代码和数据已开源。
The choice-based facility location (CBFL) problem arises in various industrial and business contexts. The problem is based 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 employed 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 the applicability of our model, 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. This repository provides data for the problem and code for the algorthm. The main folders are 'data', 'src', and 'results'. 'data': four datasets generator (and instances) used in the paper. 'src': the source code for the decomposition algorithm. 'results': high-resoultion figures in our paper All experimental results can be found in the paper (manuscript and electronic companion).