求解垃圾产生率不确定下的垃圾桶选址问题：一种双目标鲁棒优化方法
Solving the waste bin location problem with uncertain waste generation rate: a bi-objective robust optimization approach
AI总结：
针对城市生活垃圾产生率不确定导致的收集点规划难题，本文构建双目标鲁棒优化模型，同时优化网络成本与收集频率，并通过现实情景验证其解的竞争力及目标权衡分析价值。
摘要：
高效的城市生活垃圾（Municipal solid waste，MSW）系统对于现代城市提升城市生活的可持续性和宜居性至关重要。为此，决策者应认真处理MSW系统的规划阶段。然而，规划的成功取决于许多不确定性来源，这些不确定性会影响系统的关键参数，例如城市区域的垃圾产生率。基于此，本文提出一个鲁棒优化模型，用于设计收集点网络（即选址和存储容量），这些收集点是与MSW系统接触的第一环节。该模型的核心特征是双目标函数，旨在同时最小化收集点网络成本，以及收集累积垃圾所需的收集频率（作为收集成本的代理指标）。通过在一组现实实例上比较该模型的解与其确定性对应模型所得解，并考虑由不同垃圾产生率定义的不同情景，证明了该模型的价值。结果表明，在所研究的几乎所有情况下，鲁棒模型都能找到具有竞争力的解。该模型的另一个优点是允许用户探索两个目标之间的权衡。
英文摘要：
An efficient Municipal solid waste (MSW) system is critical to modern cities in order to enhance sustainability and livability of urban life. With this aim, the planning phase of the MSW system should be carefully addressed by decision makers. However, planning success is dependent on many sources of uncertainty that can affect key parameters of the system, e.g., the waste generation rate in an urban area. With this in mind, this paper contributes with a robust optimization model to design the network of collection points (i.e., location and storage capacity), which are the first points of contact with the MSW system. A central feature of the model is a bi-objective function that aims at simultaneously minimizing the network costs of collection points and the required collection frequency to gather the accumulated waste (as a proxy of the collection cost). The value of the model is demonstrated by comparing its solutions with those obtained from its deterministic counterpart over a set of realistic instances considering different scenarios defined by different waste generation rates. The results show that the robust model finds competitive solutions in almost all cases investigated. An additional benefit of the model is that it allows the user to explore trade-offs between the two objectives.
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