SRCCP: A stochastic robust chance-constrained programming model for municipal solid waste management under uncertainty

Abstract

A hybrid stochastic robust chance-constraint programming (SRCCP) model was developed in this study for supporting municipal solid waste management under uncertainty. The method improves upon the existing robust-optimization (RO) and chance-constraint programming (CCP) approaches by allowing analysis on trade-offs among expected value of the objective function, variation in the value of the objective function and the risk of violating constraints that contain uncertain parameters. SRCCP could be used to examine the balance between solution robustness and model robustness, and was especially useful for analyzing the reliability of satisfying (or risk of violating) system constraints under complex uncertainties. A long-term municipal solid waste management problem was used to demonstrate the applicability of SRCCP, with violations for capacity constraints being assumed under various significance levels. The study results demonstrated that a higher system cost may guarantee that waste-management requirements and environmental criteria be met, and a lower cost may lead to a higher risk of violating the related regulations. The proposed SRCCP model could be used by waste managers for identifying desired waste-management policies under various environmental, economic, and system-reliability constraints and complex uncertainties.