Territorial Prioritization of Urban Solid Waste Collection in Rio de Janeiro Using MCDM/SAW and Integer Programming

This paper presents a hybrid decision-support framework that combines Multi-Criteria Decision Making (MCDM) and a MILP model to prioritize territorial expansion for urban solid waste collection in Rio de Janeiro. The approach integrates public data from 2022 Census,IPS-Rio 2024, and SNIS 2022, covering 32 Administrative Regions. The study evaluates areas based on four criteria: inadequate disposal rates, social vulnerability, population, and the absolute number of households classified under inadequate disposal. A Simple Additive Weighting (SAW) model generates priority indices, which then serve as benefit coefficients in a budget-constrained binary knapsack model. Robustness is evaluated through alternative weighting and ranking methods, and comparison against simple heuristics as a baseline. Results indicate that the high-priority quartile concentrates 68.4% of households with inadequate disposal, while the full-coverage scenario encompasses 18,938 households classified under inadequate disposal. This framework provides a transparent, data-driven tool for optimizing waste management infrastructure and addressing social inequalities under budget constraints.