Environmental Management Under Uncertainty—An Internal-Parameter Two-Stage Chance-Constrained Mixed Integer Linear Programming Method

Abstract

In this study, an internal-parameter two-stage chance-constrained mixed integer linear programming (ITCILP) method is developed for municipal solid waste (MSW) management under uncertainty. The ITCILP improves upon the existing optimization methods with advantages in uncertainty reflection, policy investigation, and risk analysis. It can directly handle uncertainties presented as both internals and probability density distributions, and can thus support the assessment of the reliability of satisfying (or the risk of violating) various constraints, for accomplishing a minimizing system cost. It can also be used for analyzing various policy scenarios that are associated with different levels of economic penalties when the promised policy targets are violated. Moreover, within a multistage context, the ITCILP can facilitate dynamic analysis for capacity-expansion planning under different constraint-violation risk levels. The developed method is applied to a case study of long-term MSW management planning. The results indicate that reasonable solutions for both binary and continuous variables have been generated under different levels of constraint-violation risk. They demonstrate the practical applicability of the developed methodology.