Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/169054
Title: Service expansion for chained business facilities under congestion and market competition
Authors: Lin, Yun Hui
Tian, Qingyun
Liu, Shaojun
Keywords: Engineering::Civil engineering
Engineering::Computer science and engineering
Issue Date: 2023
Source: Lin, Y. H., Tian, Q. & Liu, S. (2023). Service expansion for chained business facilities under congestion and market competition. Computers and Operations Research, 153, 106175-. https://dx.doi.org/10.1016/j.cor.2023.106175
Journal: Computers and Operations Research
Abstract: 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.
URI: https://hdl.handle.net/10356/169054
ISSN: 0305-0548
DOI: 10.1016/j.cor.2023.106175
Schools: School of Civil and Environmental Engineering 
Rights: © 2023 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

SCOPUSTM   
Citations 50

2
Updated on Jun 6, 2024

Page view(s)

78
Updated on Jun 13, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.