ITSSIP: Interval-parameter two-stage stochastic semi-infinite programming for environmental management under uncertainty

  • Authors:

  • P. Guo;G. H. Huang;L. He;B. W. Sun

  • Affiliations:

  • Environmental Systems Engineering Program, University of Regina, Regina, SK S4S 0A2, Canada;Environmental Systems Engineering Program, University of Regina, Regina, SK S4S 0A2, Canada;Environmental Systems Engineering Program, University of Regina, Regina, SK S4S 0A2, Canada and Chinese Research Academy of Environmental Science, North China Electric Power University, Beijing 10 ...;Central University of Finance and Economics, 39 South College Road, Beijing 100081, PR China

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2008

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Abstract

In this study, an interval-parameter two-stage stochastic semi-infinite programming (ITSSIP) method is developed for municipal solid waste (MSW) management under uncertainty. In order to better account for uncertainties, the uncertainties are expressed with discrete intervals, functional intervals and probability distributions. The ITSSIP method integrates the two-stage stochastic programming (TSP), interval programming (IP), chance-constrained programming (CCP) and semi-infinite programming (SIP) within a general optimization framework. ITSSIP has infinite constraint because it uses functional intervals with time (s) being an independent variable. At the same time, ITSSIP also presents probability distribution information. The ITSSIP method can incorporate pre-regulated MSW management policies directly into its optimization process to analyze various policy scenarios having different economic penalties when the promised amounts are not delivered. The model is applied to a MSW management system with three waste treatment facilities, three cities and three periods. As an extension of mathematical programming methods, the developed ITSSIP approach has advantages in uncertainty reflection and policy analysis. Firstly, ITSSIP can help generate optimal solutions for decision variables under different levels of waste-generation rate and different levels of constraint-violation probability, which are informative for decision makers; secondly, it has the capability in addressing the parameter's dynamic feature, i.e., variations of the parameters with time; this could hardly be reflected in the previous methods. The obtained solutions are useful for decision makers to obtain insight regarding the tradeoffs between environmental, economic and system-reliability criteria.