Interval stochastic quadratic programming approach for municipal solid waste management

Abstract

In this study, an interval stochastic quadratic programming method (ISQP) is developed through incorporating techniques of chance-constrained programming (CCP) and inexact quadratic programming (IQP) within a general framework. This method improves upon the conventional IQP approaches in uncertainty reflection and risk analysis. Interval stochastic quadratic programming can handle dual uncertainties expressed as interval values and probability distributions, and can deal with nonlinearities in objective function to reflect economies-of-scale effects on the system cost. It can also support the assessment of the risk of violating various constraints, for accomplishing a minimizing system cost. The developed ISQP is applied to a municipal solid waste (MSW) management system with multiple disposal facilities and multiple cities within multiple time periods. Results of the case study indicate that useful solutions for planning MSW management practices have been generated under different probability levels of violating constraints, which are informative and flexible for decision makers. A high system cost is associated with a low risk level of violating constraints, and a low system costs will run into a high probability of violating constraints. There is a tradeoff between the system cost and the constraint-violation risk.