Service expansion for chained business facilities under congestion and market competition

• Main Authors: Lin, Yun Hui, Tian, Qingyun, Liu, Shaojun
• Other Authors: School of Civil and Environmental Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Civil engineering; Engineering::Computer science and engineering; Chained Business Operations; Service Expansion
• Online Access: https://hdl.handle.net/10356/169054
• Institution: Nanyang Technological University

We study a service expansion problem for chained business facilities under endogenic facility congestion and exogenous market competition. More specifically, we consider a company that operates a chain of facilities and plans to expand service capacities with the objective of maximizing its profit, accounting for revenue and expansion costs. To estimate revenue, the company needs to anticipate customer behaviors. Due to the co-existence of competition and congestion, customer behaviors are explained as a two-stage process. In the first stage, customers make “channel” choices, i.e., they decide whether to seek services from the company. Such a choice reflects the market competition and is predicted by a discrete choice model. Subsequently, customers who select the company will choose one facility to patronize. Owing to congestion, the facility choice will induce “user equilibrium”, which in return affects the outcome of market competition. To facilitate the company's decision-making in this complex business environment, we develop a generic modeling framework. Unfortunately, the proposed model is nonconvex. To solve it, we first design an approximate mixed-integer linear programming approach subject to adjustable approximation errors. We then propose a surrogate optimization framework for large-scale instances, which explores the hidden bilevel structure of the model and leverages a “learning-to-optimize” problem and a customer behavior estimation subroutine. Using extensive computational experiments, we demonstrate the effectiveness of the proposed approaches. Finally, we conduct sensitivity analysis and draw practical implications.