Abstract
The COVID-19 pandemic placed unprecedented strain on medical waste management systems, increasing waste volume and hazard levels, overwhelming disposal infrastructure, and creating serious public health and environmental risks. Addressing these challenges requires efficient waste collection and transportation strategies. This study proposes a sustainable multi-objective location-routing model for medical waste management during pandemics, integrating four conflicting goals: minimizing routing risk, travel time, time window violations, and CO₂ emissions, balancing social, economic, and environmental dimensions. A key feature is routing risk assessment to prioritize safer transportation paths, combined with a fuzzy chance-constrained programming approach using triangular fuzzy numbers to address demand uncertainty. This design reflects crisis volatility and, using simulated real urban network data, offers a resilient and practical model for data-scarce pandemic contexts. Validation involved computational experiments on artificial instances: small-scale problems were solved via the Epsilon-constraint method in GAMS with CPLEX, while medium and large-scale problems were addressed using two metaheuristics, NSGA-II and MOPSO, compared across six performance criteria, including non-dominated solution count, mean ideal distance, diversity, and coefficient of variation. Results highlight the decisive impact of financial resources, showing that a 15 % budget cut makes the problem unsolvable, underscoring the need for resilient planning and minimum threshold budgeting. NSGA-II consistently outperformed MOPSO in solution quality and efficiency. Sensitivity analysis on budget, carbon limits, and route structures offers guidance for urban managers and health authorities to improve operational efficiency, system resilience, and environmental protection. This framework supports optimal resource allocation and low-risk route design, with potential influence on environmental policy.
