Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX 78713, USA.
J Hazard Mater. 2013 Feb 15;246-247:257-66. doi: 10.1016/j.jhazmat.2012.12.036. Epub 2012 Dec 28.
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.
温室气体（GHG）排放来自城市固体废物（MSW）管理设施已成为一个严重的环境问题。在 MSW 管理中，不仅要考虑经济目标，还要同时考虑环境目标。在这项研究中，开发了一种动态随机可能性多目标规划（DSPMP）模型，用于支持 MSW 管理和相关的 GHG 排放控制。通过将模糊可能性规划和机会约束规划纳入一般混合整数多目标线性规划（MOP）框架中，该 DSPMP 模型改进了现有的废物管理优化方法，其中可以有效地反映各种表示为模糊可能性分布和概率分布的不确定性。综合考虑了两个相互冲突的目标，包括系统总成本最小化和废物管理设施的总 GHG 排放最小化。分析并比较了三个规划方案，代表了决策者在综合 MSW 管理中对经济发展和环境影响（即 GHG 排放）问题的不同偏好。针对三个方案和不同的 p(i)水平（表示约束条件被违反的概率）生成了最优决策方案，用于规划废物流量分配和设施容量扩展以及 GHG 排放控制。结果表明，通过提出的 DSPMP 模型可以有效地反映经济和环境之间的权衡。生成的决策变量可以帮助决策者根据其隐含的知识和偏好证明和/或调整其废物管理策略。
