Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2024
Abstract
This paper addresses the challenge of optimal retail expansion in competitive urban environments through a novel approach to the Competitive Facility Location (CFL) problem. Traditional methods for solving CFL problems often struggle with large-scale scenarios, relying on manual pre-selection of candidate sites and imposing limitations on the number of new locations. Our approach leverages Adaptive Large Neighborhood Search (ALNS) enhanced with data enrichment techniques, including community detection on road networks and population weighting based on mobility data. We developed two ALNS variants: Community Geometric Centroid (CGC-ALNS) and Population Weighted Centroid (PWC-ALNS). These methods automate site selection, eliminating manual pre-selection while enabling the evaluation of numerous potential store locations. Benchmarking against ArcGIS, a widely used commercial CFL software, reveals significant performance improvements. CGC-ALNS outperforms ArcGIS with up to a 2% increase in consumer capture, while PWC-ALNS achieves an average increase of 4.6% to 13.1% across diverse store distribution scenarios. Key contributions include an automated, data-driven site selection process unrestricted by the number of new sites and notable performance enhancements over existing commercial solutions.
Keywords
competitive facility location, retail expansion, Adaptive Large Neighborhood Search, data-driven optimization, community detection, population weighting, mobility data, site selection, ArcGIS benchmarking, urban planning
Discipline
Databases and Information Systems | Operations Research, Systems Engineering and Industrial Engineering
Research Areas
Intelligent Systems and Optimization
Publication
2024 IEEE International Conference on Big Data (IEEE BigData): Washington, DC, December 15-18: Proceedings
First Page
1
Last Page
6
ISBN
9798350362480
Identifier
10.1109/BigData62323.2024.10825526
Publisher
IEEE
City or Country
Piscataway
Citation
TAN, Ming Hui; TAN, Kar Way; and LAU, Hoong Chuin.
A data-driven approach for automated multi-site competitive facility location. (2024). 2024 IEEE International Conference on Big Data (IEEE BigData): Washington, DC, December 15-18: Proceedings. 1-6.
Available at: https://ink.library.smu.edu.sg/sis_research/9705
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/BigData62323.2024.10825526
Included in
Databases and Information Systems Commons, Operations Research, Systems Engineering and Industrial Engineering Commons