An Air Quality Management Model Based on an Interval Dual Stochastic-Mixed Integer Programming

The issue of air pollution has become the focus of the world because of its significant influence to the economic development and public health. This paper proposes an interval dual stochastic-mixed integer programming (IDSIP) approach for regional air quality management. The IDSIP approach can be effectively communicated into the optimization processes and resulting solutions, which is formulated through integrating interval-parameter integer programming (IIP) within a two-stage stochastic programming (TSP) joint chance-constrained programming (CCP) and could deal with uncertainties expressed as not only probability distributions but also interval values. Moreover, the left-hand-side (LHS) constraints with stochastic variables could be handled at different risk levels with varied reliability scenarios. In the modeling formulation, penalties are imposed when expected policies are violated. The results indicate that reasonable solutions for air quality management system have been generated, which can help decision makers draw up productive strategies taking into account the trade-off between system economy and air quality under uncertainty.