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Siting and Routing Assessment for Solid Waste Management Under Uncertainty Using the Grey Mini-Max Regret Criterion

机译:灰色最小-最大后悔准则下不确定性固体废物管理选址与选路评估

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Solid waste management (SWM) is at the forefront of environmental concerns in the Lower Rio Grande Valley (LRGV), South Texas. The complexity in SWM drives area decision makers to look for innovative and forward-looking solutions to address various waste management options. In decision analysis, it is not uncommon for decision makers to go by an option that may minimize the maximum regret when some determinant factors are vague, ambiguous, or unclear. This article presents an innovative optimization model using the grey mini-max regret (GMMR) integer programming algorithm to outline an optimal regional coordination of solid waste routing and possible landfill/incinerator construction under an uncertain environment. The LRGV is an ideal location to apply the GMMR model for SWM planning because of its constant urban expansion, dwindling landfill space, and insufficient data availability signifying the planning uncertainty combined with vagueness in decision-making. The results give local decision makers hedged sets of options that consider various forms of systematic and event-based uncertainty. By extending the dimension of decision-making, this may lead to identifying a variety of beneficial solutions with efficient waste routing and facility siting for the time frame of 2005 through 2010 in LRGV. The results show the ability of the GMMR model to open insightful scenario planning that can handle situational andrndata-driven uncertainty in a way that was previously unavailable. Research findings also indicate that the large capital investment of incineration facilities makes such an option less competitive among municipal options for landfills. It is evident that the investment from a municipal standpoint is out of the question, but possible public-private partnerships may alleviate this obstacle.
机译:在南德克萨斯州下里奥格兰德河谷(LRGV),固体废物管理(SWM)处于环境关注的最前沿。 SWM的复杂性促使地区决策者寻求创新且具有前瞻性的解决方案来解决各种废物管理方案。在决策分析中,决策者通常会选择在某些决定因素模糊,模棱两可或不清楚的情况下最大程度地减少最大遗憾的选择。本文提出了一个创新的优化模型,该模型使用灰色最小-最大后悔(GMMR)整数规划算法来概述不确定环境下固体废物路线的最佳区域协调以及可能的垃圾填埋场/焚化炉建设。 LRGV是GMMR模型用于SWM规划的理想场所,因为它的城市规模不断扩大,垃圾填埋场不断缩小,数据可用性不足,这表明规划的不确定性以及决策的模糊性。结果为当地决策者提供了套期保值的选项,这些选项考虑了各种形式的系统性和基于事件的不确定性。通过扩大决策范围,这可能会导致在LRGV的2005年至2010年的时间范围内,通过有效的废物处理和设施选址确定各种有益的解决方案。结果表明,GMMR模型能够打开有洞察力的方案计划,该计划能够以以前无法获得的方式处理情况和数据驱动的不确定性。研究结果还表明,焚化设施的巨额资本投入使得这种选择在市政填埋场选择中的竞争力较弱。显然,从市政的角度来看投资是不可能的,但是可能的公私合作伙伴关系可以减轻这一障碍。

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