Network-Based Optimization of Municipal Solid Waste Bin Allocation for Improving Urban Service Accessibility

Abstract Rapid urbanization in midsized Indian cities has intensified challenges in municipal solid waste (MSW) management, particularly the inefficient and inequitable distribution of public waste-collection infrastructure. Existing bin networks often provide limited-service accessibility, concentrate along major roads, and encroach upon environmentally or socially sensitive areas. To address these challenges, this study aimed to evaluate the existing waste-bin network and develop an optimized allocation framework for improving service accessibility and environmental suitability within a mixed-use urban cluster of Kota city, India. A GIS-based network-constrained location-allocation framework was developed by integrating establishment-level demand weighting, road-network accessibility, environmental exclusion constraints, and dual-bin allocation for general and recyclable waste streams. Recycling-bin allocation specifically targeted dry recyclable fractions generated by commercial and institutional establishments. Optimized locations were validated using high-resolution satellite imagery, while model robustness was evaluated through sensitivity analysis under varying service-distance thresholds. The optimized configuration increased establishment coverage from 40.3% to 91.1%, and spatial service coverage increased from 32.4% to 69.7%. Coverage of critical facilities improved substantially, with hospital coverage increasing from 18.5% to 74.1% and school coverage increasing from 29.2% to 87.5%. Commercial establishments achieved 93.4% service coverage under the dual-bin allocation scenario. Sensitivity analysis indicated stable allocation performance, with coverage variation remaining below 5% across different service-distance assumptions. The proposed framework demonstrates significant potential for improving accessibility, spatial equity, recycling integration, and operational efficiency in urban MSW systems, while providing a practical and scalable decision-support tool for waste-infrastructure planning in rapidly urbanizing and resource-constrained cities.