Optimization of environmental management strategies through a dynamic stochastic possibilistic multiobjective program

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• 作者：Xiaodong Zhang^a ; ^{xiaodong.zhang@beg.utexas.edu} ; ^{zxd@iseis.org} ; Gordon Huang^b
• 关键词：Municipal solid waste management ; Greenhouse gas emission control ; Fuzzy possibilistic programming ; Multiobjective linear programming
• 刊名：Journal of Hazardous Materials
• 出版年：2013
• 出版时间：15 February, 2013
• 年：2013
• 卷：246-247
• 期：Complete
• 页码：257-266
• 全文大小：641 K

文摘

Greenhouse gas (GHG) emissions from municipal solid waste (MSW) management facilities have become a serious environmental issue. In MSW management, not only economic objectives but also environmental objectives should be considered simultaneously. In this study, a dynamic stochastic possibilistic multiobjective programming (DSPMP) model is developed for supporting MSW management and associated GHG emission control. The DSPMP model improves upon the existing waste management optimization methods through incorporation of fuzzy possibilistic programming and chance-constrained programming into a general mixed-integer multiobjective linear programming (MOP) framework where various uncertainties expressed as fuzzy possibility distributions and probability distributions can be effectively reflected. Two conflicting objectives are integrally considered, including minimization of total system cost and minimization of total GHG emissions from waste management facilities. Three planning scenarios are analyzed and compared, representing different preferences of the decision makers for economic development and environmental-impact (i.e. GHG-emission) issues in integrated MSW management. Optimal decision schemes under three scenarios and different p_i levels (representing the probability that the constraints would be violated) are generated for planning waste flow allocation and facility capacity expansions as well as GHG emission control. The results indicate that economic and environmental tradeoffs can be effectively reflected through the proposed DSPMP model. The generated decision variables can help the decision makers justify and/or adjust their waste management strategies based on their implicit knowledge and preferences.