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学术前沿讲座——Locating facilities under market competition and decentralized consumer behavior



Locating facilities under market competition and decentralized consumer behavior












Choice-Based Facility Location (CBFL) arises in various industrial and business contexts, focusing on strategically locating service facilities to improve market competitiveness or achieve strategic objectives. The problem stands on a decentralized perspective, where individual customers make their own decisions regarding which company or facility to visit, based on their specific preferences. In this regard, the company is unable to dictate customer allocation to facilities in a centralized manner but instead must rely on designing effective service profiles to attract and retain customers. In this seminar, we will start by exploring the practical applications of CBFL, including the placement of parcel locker stations for last-mile delivery, the design of omnichannel fulfillment networks, and the strategic planning of urban park-and-ride transportation systems. Overall, CBFL can be classified into two streams, i.e., CBFL under static competition and CBFL under leader-follower competition. Static competition focuses on a single company with the assumption that competing companies will not change their services during the planning horizon. This stream has been studied extensively, giving rise to a large number of decision models. To facilitate the decision-making of partitioners, this talk provides a unified model, which can represent most of the CBFL problems and can be solved efficiently by decomposition algorithms. On the contrary, leader-follower competition studies multiple companies, and each company is assumed to maximize its own objective. There exist interactive decisions at the company level. The related problems are typically formulated using bilevel programming. In this talk, we discuss a general bilevel model for CBFL under leader-follower competition. Efficient algorithms are proposed and tested on various datasets to draw managerial implications.


林云辉,本科就读于东南大学交通学院,2021年于新加坡国立大学工业系统工程与管理系获得博士学位。曾任都柏林大学商学院助理教授。202210月起,就职于新加坡科技研究局(A*STAR)。主要的理论研究方向为离散优化和双层规划。应用研究包括智能选址,城市交通,和连锁商业运营等。2019年获得The 3rd International Symposium on Multimodal Transportation的最佳学生论文奖。到目前为止,他在Production and Operations Management (POMS), INFORMS Journal on Computing (IJOC), Transportation Research Part B: Methodological (TRB), European Journal of Operational Research (EJOR), Transportation Research Part E: Logistics and Transportation Review (TRE), Transportation Research Part C: Emerging Technologies (TRC), Computers & Operations Research (COR), 等期刊发表20余篇论文。并长期担任TS, TRB, EJOR, TRE等期刊的审稿人。