Locating facilities under competition and market expansion: Formulation, optimization, and implications

Abstract

As the ongoing battle between brick‐and‐mortar stores and e‐commerce shops escalates, managers of the former are becoming more cautious regarding their strategic store site selection and decoration decisions, particularly if foreseeable competition from rival companies exists. This paper investigates a bilevel competitive facility location (BCFL) problem, where two companies, a leader and a follower, plan to enter a market sequentially. Each company has a budget to open and design facilities. The goal is to maximize expected revenue that is forecasted through a discrete choice model. To reflect a practical environment, we further consider a situation with elastic demand, explaining the market expansion effect when customers are offered better service due to the opening of new facilities. We formulate the problem as a nonlinear 0‐1 bilevel program. Because of the bilevel structure and the market expansion effect, this problem is so challenging that we are unaware of any exact algorithms in the literature. Motivated by this gap, we develop an exact framework that leverages the state‐of‐the‐art value‐function–based approach. However, this framework requires solving a mixed‐integer nonconvex optimization problem (MINOP) at each iteration, which is computationally prohibitive even for medium‐scale instances. To mitigate the intractability, we propose a new framework that avoids MINOP and tackles instances with hundreds of location variables. Finally, we conduct extensive computational studies to show the efficiency and effectiveness of our method as well as provide insightful guidance for managers to have win–win/dominate outcomes and choose an appropriate market size function when dealing with expansion decisions in chained business operations.

Unified Framework for Choice-Based Facility Location Problem

“…More specifically, customers split their buying power/demand proportionally among the facilities in the consideration set plus the outside option. As a result, customer choices are interpreted probabilistically, and the probability of customer i seeking service from the company is estimated in proportion to the total utility of the facilities in the consideration set and the representative utility of the outside option (Aboolian et al 2007, Lin et al 2022b), that is,…”

Robust competitive facility location model with uncertain demand types

“…Yu and Khan [ 27 ] built an evolutionary game model based on the relationship between agricultural product suppliers and urban residents in the financing system. Lin et al [ 28 ] investigates a bilevel competitive facility location problem to maximize expected reveue by considering a discrete choice rule. Latifi et al [ 29 ] studied the leader-follower competitive facility location problem in a closed-loop supply chain, where customer behavior is the Huff gravity-based rule.…”