CN EN
Advanced

A Single-facility Competitive Location Problem in the Plane Based on Customer Choice Rules (CEIBS Working Paper, No. 021/2020/POM/DEC, 2020)

Abstract

Since customer choice rules would greatly affect the performance of retail facilities, they
should be considered when a chain wants to locate a new facility in a competitive market.
In the existing studies, customers’ choice behavior is usually considered as homogeneous,
which means that all customers patronize facilities with one kind of customer choice rules:
the deterministic rule, the probabilistic rule or the multi-deterministic rule. However, it
is not in line with reality as we have investigated people’s choice behavior on convenience
stores by questionnaire surveys, and survey results show that different customers may patronize facilities with different choice rules. In order to study competitive facility location
problems in which customers’ choice behavior is heterogeneous, we classify customers as
three types by customer choice rules, the relative proportions of which are calculated based
on questionnaires. A customer classification based competitive facility location model in
the plane is proposed in which location and quality of the new facility are to be determined
in order to maximize the profit of the locating chain. Since the model is non-convex and
discontinuous, and location problems in practice are usually large-scale, four kinds of heuristic algorithms instead of exact algorithms are designed for obtaining a satisfactory solution
including Particle Swarm Optimization, Tabu Search, Simulated Annealing and Genetic
Algorithm. Numerical experiments show that Particle Swarm Optimization performs best
both in computation efficiency and solution precision. Comparisons among location results
employing different customer proportions reveal that customer proportion significantly affects location results. Most importantly, the locating chain may lose large profit once the
customer proportion is wrongly estimated. Maximum profit loss is more than 20% in our
cases.

Keyword

Author Community

[Ma, Hongguang; Li, Xiang; Guan, Xiaoyu] Beijing University of Chemical Technology

[Zhao, Xiande; Wang, Liang] China Europe International Business School (CEIBS)


Related Article

Source

Year:2020

Publish Date:2020-06

Language:English

Cited Count
W
Loading...
C
Loading...
Get Fulltext
Rights and Licenses
Related Keywords
Communities & Collections
Access Stats
Creative Commons Licence
The content of CEIBS Research Online is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).