Abstract
An interval-parameter fuzzy-stochastic programming (IPFSP) approach is developed for planning air quality management systems under uncertainty. Fuzzy sets theory is introduced to represent uncertainties existing in various operation costs under different loading conditions. Compared with the existing approaches, the proposed IPFSP performs uniqueness through two special features: one is it could provide more feasible control strategies under different upcoming pollutant amounts, which was seldom considered in the previous research efforts; the other is, as a result of interval-parameter programming (IPP) and two-stage stochastic programming (TSP) being incorporated into the modeling framework, uncertain information expressed as discrete intervals and probability density functions can be effectively reflected. After formulating the model, a representative regional air quality management system is provided for demonstrating its applicability. The results indicate that reasonable solutions are obtained, and optimal management strategies with minimized system operation cost are generated for facilitating decision-making. Of more importance, the developed approach presents high efficiency in handling complex dissatisfactory data availability and enhancing system flexibility.