SRFILP: A Stochastic Robust Fuzzy Interval Linear Programming Model for Municipal Solid Waste Management under Uncertainty

Abstract

A stochastic robust fuzzy interval linear programming (SRFILP) model was proposed for supporting municipal solid waste (MSW) management under multiple uncertainties. The method integrated stochastic robust optimization (SRO), interval linear programming (ILP) and fuzzy possibilistic programming (FPP) methods into a general framework and could simultaneously deal with uncertainties expressed as fuzzy sets, stochastic variables and discrete intervals. The SRFILP model was applied to a hypothetical problem of municipal solid waste management. The results demonstrated that flexible interval solutions under different I±-cut levels could be generated, which could help decision makers gain an in-depth insight into system complexities associated with solid waste management. The waste-management alternatives could be generated by adjusting the decision-variable values within their solution intervals. In addition, the proposed method could be used to help evaluate the trade-off between solution robustness and model robustness, and help waste managers identify desired cost-effective policies considering environmental, economic, system-feasibility and system-reliability constraints.