Optimizing medical waste collection in urban systems: A constraint programming approach for sustainable public sector decision-making

This study addresses the optimization of medical waste collection (MWC) in Ho Chi Minh City, Vietnam, where increasing waste volumes pose challenges for efficiency and sustainability. Using real-world operational data from Citenco, we formulate the problem as a capacitated vehicle routing problem (CVRP) with stochastic demand, solved through a combination of constraint programming and chance-constrained programming. The proposed model reduces total travel distance by 22%, travel time by 10%, and increases vehicle load utilization by 6%, while lowering the number of daily trips to treatment facilities. Sensitivity analysis confirms robustness under varying service levels and expanded coverage. These results provide evidence-based insights for policymakers and public waste management agencies, supporting sustainable decision-making in urban medical waste collection. • Medical waste collection is a challenging problem that can lead to hazardous impacts on environment. • Inefficiency in collecting route, which is also known as Waste Collection Vehicle Routing Problem, can be minimized by Chance-Constrained Programming. • Optimization model was developed to improve efficiency and test for robustness vs demand fluctuation. • Medical waste collection of Citenco Ltd., a major waste collector in Ho Chi Minh City, Vietnam, was optimized using the optimization model. • Sensitivity analysis for two scenarios, increased waste demand and increased collection points, were done.