An interval-parameter two-stage stochastic integer programming model for environmental systems planning under uncertainty

Abstract

An interval-parameter two-stage stochastic mixed integer programming (ITMILP) technique is developed for waste management under uncertainty. It is a hybrid of inexact two-stage stochastic programming and mixed integer linear programming methods. The ITMILP method can directly handle uncertainties expressed not only as probability density functions but also as discrete intervals. It can be used to analyse various policy scenarios that are associated with different levels of economic penalties when the promised policy targets are violated. More importantly, it can facilitate dynamic analysis of decisions on capacity expansion planning within a multi-region, multi-facility, multi-period, and multi-option context. The results will help to generate a range of decision alternatives under various system conditions, and thus offer insight into the trade-offs between environmental and economic objectives. The ITMILP method is applied to planning facility expansion and waste flow allocation within a waste management system. The results indicate that reasonable solutions have been generated for both binary and continuous variables. The binary-variable solutions represent the decisions of facility expansion, while the continuous-variable solutions are related to decisions on waste flow allocation.