Municipal solid waste supply chain optimization for value-added product development under uncertainty

Abstract

Optimizing municipal solid waste (MSW) management through the production of valuable products and energy conversion is crucial to mitigate environmental damage and promote economic sustainability. This study focuses on addressing the MSW supply chain problem by exploring the optimal location for the waste treatment. The supply chain network encompasses MSW transfer stations, treatment facilities, and markets with product demands. The methodological approach entails constructing a superstructure, gathering relevant data, and analyzing the results. Both deterministic MILP and two stage stochastic model are used in this study. A deterministic mixed-integer linear programming (MILP) model is employed to optimize the MSW supply chain problem, with the use of solver BARON. To account for uncertainties in supply–demand and transportation costs, a two-stage stochastic MILP model is developed. The deterministic equivalent approach is then employed to solve the stochastic model, resulting in an average solution across all scenarios. The decision variable pertaining to the selection of treatment technology locations is managed in the first stage. The second stage focuses on determining transportation and production-related decisions. Stochastic models can capture the inherent unpredictability of real-world systems by simulating a range of potential scenarios, helping to tackle uncertainty. To underscore the practical relevance of the mathematical programming formulation, a case study is presented and thoroughly analyzed.