ISMISIP: an inexact stochastic mixed integer linear semi-infinite programming approach for solid waste management and planning under uncertainty |
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| Authors: | P Guo G H Huang L He |
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| Institution: | (1) Environmental Systems Engineering Program, University of Regina, Regina, SK, S4S 0A2, Canada;(2) Chinese Research Academy of Environmental Science, North China Electric Power University, Beijing, 100012-102206, China;(3) Center for Studies in Energy and Environment, University of Regina, Regina, SK, S4S 0A2, Canada |
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| Abstract: | An inexact stochastic mixed integer linear semi-infinite programming (ISMISIP) model is developed for municipal solid waste
(MSW) management under uncertainty. By incorporating stochastic programming (SP), integer programming and interval semi-infinite
programming (ISIP) within a general waste management problem, the model can simultaneously handle programming problems with
coefficients expressed as probability distribution functions, intervals and functional intervals. Compared with those inexact
programming models without introducing functional interval coefficients, the ISMISIP model has the following advantages that:
(1) since parameters are represented as functional intervals, the parameter’s dynamic feature (i.e., the constraint should
be satisfied under all possible levels within its range) can be reflected, and (2) it is applicable to practical problems
as the solution method does not generate more complicated intermediate models (He and Huang, Technical Report, 2004; He et al. J Air Waste Manage Assoc, 2007). Moreover, the ISMISIP model is proposed upon the previous inexact mixed integer linear semi-infinite programming (IMISIP)
model by assuming capacities of the landfill, WTE and composting facilities to be stochastic. Thus it has the improved capabilities
in (1) identifying schemes regarding to the waste allocation and facility expansions with a minimized system cost and (2)
addressing tradeoffs among environmental, economic and system reliability level. |
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| Keywords: | Chance-constrained programming Decision making Functional interval Semi-infinite programming Uncertainty |
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