A Data-Driven Approach for Automated Multi-Site Competitive Facility Location

This paper addresses the challenge of optimizing large-scale retail expansion in competitive urban environments through a data-driven and automated approach to the Competitive Facility Location (CFL) problem. Traditional CFL methods often face limitations in handling large-scale scenarios, relying on manual pre-selection of candidate sites and imposing restrictions on the number of new locations. Our approach uses Adaptive Large Neighborhood Search (ALNS) enhanced with data enrichment techniques, such as community detection on road networks and population weighting based on mobility data. We developed 2 ALNS variants: Community Geometric Centroid (CGC-ALNS) and Population Weighted Centroid (PWC-ALNS). These methods automate the site selection process, eliminating the need for manual pre-selection and enabling evaluation of a large number of store locations. We benchmarked our approaches against ArcGIS, a widely used commercial software for CFL problems. The results demonstrate notable improvements in performance: CGC-ALNS consistently outperforms ArcGIS with up to a 2% increase in consumer count captured, while PWCALNS achieves even greater gains, with an average increase of 4.6% to 13.1% across various store distribution scenarios. Our key contributions include an automated, data-driven site selection process with no restrictions on the number of new sites, and significant performance improvements over existing commercial solutions.