Municipal Solid Waste Management under Uncertainty: An Interval-Fuzzy Two-Stage Stochastic Programming Approach

Abstract

In this study, an interval-fuzzy two-stage stochastic linear programming (IFTP) method is developed for planning waste-management systems under uncertainty. In the IFTP, approaches of two-stage stochastic programming, interval-parameter programming, and fuzzy linear programming are integrated into a general optimization framework to effectively tackle uncertainties described in terms of probability density functions, fuzzy membership functions and discrete intervals. The IFTP method can incorporate pre-regulated waste management policies directly into its optimization process, and be used for analyzing various policy scenarios that are associated with different levels of economic penalties when the promised policy targets are violated. It can also help quantify the satisfaction degrees of the system objective and constraints under uncertainty, as defined in the obtained solutions. The IFTP model can be transformed into two deterministic submodels based on an interactive algorithm. Interval solutions, which are stable in the given decision space with varying levels of system-failure risk, can then be obtained by solving the two submodels sequentially. Then, the developed method is applied to a case study of waste allocation within a municipal solid waste management system. The results indicate that reasonable solutions have been generated. They can be used to generate decision alternatives and help MSW managers to identify desired policies under various environmental, economic, and system-reliability conditions

Keywords: decision-making, environment, fuzzy, interval optimization, solid waste, two-stage, uncertainty