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Sustainable pattern analysis of a publicly owned material recovery facility in a fast-growing urban setting under uncertainty

机译：不确定性下快速发展的城市环境中的公共物资回收设施的可持续模式分析

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摘要

Sustainable development goals are achievable through the installation of Material Recovery Facilities (MRFs) in certain solid waste management systems, especially those in rapidly expanding multi-district urban areas. MRFs are a cost-effective alternative when curbside recycling does not demonstrate long-term success. Previous capacity planning uses' mixed integer programming optimization for the urban center of the city of San Antonio, Texas to establish that a publicly owned material recovery facility is preferable to a privatized facility. As a companion study, this analysis demonstrates that a MRF alleviates economic, political, and social pressures facing solid waste management under uncertainty. It explores the impact of uncertainty in decision alternatives in an urban environmental system. From this unique angle, waste generation, incidence of recyclables in the waste stream, routing distances, recycling participation, and other plunning components are taken as intervals to expand upon previous deterministic integer-programming models. The information incorporated into the optimization objectives includes economic impacts for recycling income and cost components in waste management. The constraint set consists of mass balance, capacity limitation, recycling limitation, scale economy, conditionality, and relevant screening restrictions. Due to the fragmented data set, a grey integer programming modeling approach quantifies the consequences of inexact information as it propagates through the final solutions in the optimization process. The grey algorithm screens optimal shipping patterns and an ideal MRF location and capacity. Two case settings compare MRF selection policies where optimal solutions exemplify the value of grey programming in the context of integrated solid waste management.
机译：通过在某些固体废物管理系统中安装材料回收设施（MRF），尤其是在快速扩展的多区市区的系统，可以实现可持续发展目标。当路边回收不能证明长期成功时，MRF是一种经济有效的选择。先前的容量规划使用了德克萨斯州圣安东尼奥市市中心的混合整数规划优化，以确立公有物资回收设施优于私有化设施。作为一项伴随研究，该分析表明，MRF缓解了不确定性下固体废物管理面临的经济，政治和社会压力。它探讨了不确定性对城市环境系统中决策选择的影响。从这个独特的角度出发，将废物产生，废物流中可回收物的发生率，路线距离，回收参与以及其他艰辛的组件作为间隔，以扩展先前的确定性整数编程模型。纳入优化目标的信息包括对废物管理中回收收入和成本成分的经济影响。约束集包括质量平衡，容量限制，回收利用限制，规模经济，条件和相关的筛选限制。由于数据集零散，因此，灰色整数编程建模方法可以量化不精确信息在优化过程中通过最终解决方案传播时的后果。灰色算法筛选出最佳的运输方式以及理想的MRF位置和容量。两个案例设置比较了MRF选择策略，其中最佳解决方案体现了集成固体废物管理中灰色编程的价值。

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