In recent decades, the explosion of the waste generation rate and corresponding environmental impacts worldwide have turned waste management into one of the most vital services in urban areas to alleviate the waste-related issues. In this study, a novel integrated model is developed to improve the municipal solid waste system by considering the facility location, shift scheduling, and vehicle routing decisions. The problem is formulated as a tri-objective mixed-integer linear programming model, striving to optimize the sustainable development goals in the waste system. These objectives encompass the total profit, air pollution emissions, citizen satisfaction, and social risk factors. The findings from this study illustrate that the proposed integrated framework empowers decision makers to maintain the resilience of the municipal solid waste system by concurrently addressing three critical sustainability aspects.

Rapid urbanization and increasing waste generation have intensified challenges in Municipal Solid Waste Management (MSWM), necessitating sustainable and efficient strategies. This study proposes an integrated optimization model to improve the economic, environmental, and social performance of MSWM. The model simultaneously addresses facility location, waste collection scheduling, and routing decisions. Transportation and logistics constraints are key barriers, and to enhance system responsiveness, the model incorporates real-time data through Internet of Things (IoT) technologies. A suite of Genetic Algorithm (GA)-based metaheuristics, namely MOGA, NSGA-II, and NSGA-III, is applied to solve the large-scale problem. Extensive numerical experiments and statistical analyses are conducted to compare the performance of algorithms under varying scenarios. Results demonstrate that NSGA-III consistently outperforms other methods, particularly in large-scale instances, by yielding better convergence and diversity in Pareto-optimal solutions. The proposed approach offers valuable insights for designing sustainable MSWM systems by reducing operational costs, environmental impacts, and improving service reliability.