Fuzzy optimization of municipal solid waste collection routing under uncertain emissions

The uncertainty in municipal solid waste (MSW) emissions poses significant challenges to collection and transportation operations, causing vehicles to be under-loaded or overloaded; in some cases, waste may not be cleared in a timely manner, thereby affecting residents’ quality of life. To study the impact of uncertain waste emissions on MSW operations, this paper investigates the MSW vehicle routing problem from a fuzzy programming perspective. Firstly, based on fuzzy credibility theory, trapezoidal fuzzy numbers are introduced to represent the waste emissions at collection points, and a multi-depot MSW routing optimization model is formulated to minimize operational cost while incorporating the decision maker’s subjective preference constraints. Then, an improved adaptive large neighborhood search algorithm (ALNS-TS) is developed by combining 12 neighborhood criteria with a tabu search (TS) mechanism to enhance global search capability. Subsequently, case studies compare routing schemes under deterministic and uncertain emissions, evaluate multiple intelligent optimization algorithms in terms of solution quality and computational efficiency, and conduct a sensitivity analysis with respect to the subjective preference values. Finally, specific and effective managerial recommendations are provided to support practical decision-making in MSW collection and transportation operations. This study effectively addresses the challenges posed by uncertain waste emissions and offers value for MSW managers.