Municipal solid waste management and greenhouse gas emission control through an inexact optimization model under interval and random uncertainties

Rapid increases in the amounts of untreated municipal solid waste (MSW) and discharged greenhouse gases (GHGs) from waste treatment processes have caused great challenges to urban development and environmental protection. This study aimed to develop an integrated interval-stochastic optimization model to support regional MSW management in China, where the parameters which show large variations were expressed as birandom variables, whereas the parameters with small fluctuation were assumed to be interval numbers. The main objective of this optimization model was to minimize the total cost with simultaneous considerations of MSW treatment and GHG control requirements. The stochastic equilibrium-based chance constrained programming technique and interactive two-step algorithms were used to solve this model. Among many types of solutions with various constraint-violation levels, the solutions with balanced characteristics were recommended as the decision basis. Compared with existing management schemes, the model solutions showed advantages in cost reduction and climate-change impact mitigation.