Purpose This study aims to identify optimal locations for public trash bins in Jeonju City, where illegal dumping has become a growing issue. Unlike traditional supply-driven methods, it uses real enforcement data to improve service coverage in unders...
Purpose This study aims to identify optimal locations for public trash bins in Jeonju City, where illegal dumping has become a growing issue. Unlike traditional supply-driven methods, it uses real enforcement data to improve service coverage in underserved areas.
Methods A Set Covering Location Model (SCLM) was applied using illegal dumping records as demand points. Candidate sites were selected based on population density, pedestrian flow, and commercial activity. GIS analysis and mathematical programming evaluated multiple scenarios with varying constraints.
Results The model identified priority sites that covered the most violations with the fewest installations. Scenario analysis revealed trade-offs between cost, coverage, and policy constraints.
Conclusion The results demonstrate that spatial optimization using real enforcement data can significantly improve the efficiency and fairness of public waste management. The findings offer practical, data-driven policy alternatives for cities facing similar challenges in waste disposal.