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一种用于危险废物定位-路径问题的改进多目标规划与增强型ε-约束方法

An Improved Multi-Objective Programming with Augmented ε-Constraint Method for Hazardous Waste Location-Routing Problems.

作者信息

Yu Hao, Solvang Wei Deng

机构信息

Department of Industrial Engineering, Faculty of Engineering Science and Technology, UiT-The Arctic University of Norway, Narvik 8505, Norway.

出版信息

Int J Environ Res Public Health. 2016 May 31;13(6):548. doi: 10.3390/ijerph13060548.

Abstract

Hazardous waste location-routing problems are of importance due to the potential risk for nearby residents and the environment. In this paper, an improved mathematical formulation is developed based upon a multi-objective mixed integer programming approach. The model aims at assisting decision makers in selecting locations for different facilities including treatment plants, recycling plants and disposal sites, providing appropriate technologies for hazardous waste treatment, and routing transportation. In the model, two critical factors are taken into account: system operating costs and risk imposed on local residents, and a compensation factor is introduced to the risk objective function in order to account for the fact that the risk level imposed by one type of hazardous waste or treatment technology may significantly vary from that of other types. Besides, the policy instruments for promoting waste recycling are considered, and their influence on the costs and risk of hazardous waste management is also discussed. The model is coded and calculated in Lingo optimization solver, and the augmented ε-constraint method is employed to generate the Pareto optimal curve of the multi-objective optimization problem. The trade-off between different objectives is illustrated in the numerical experiment.

摘要

危险废物选址-路径规划问题因对附近居民和环境存在潜在风险而具有重要意义。本文基于多目标混合整数规划方法开发了一种改进的数学模型。该模型旨在协助决策者选择不同设施的位置,包括处理厂、回收厂和处置场,提供危险废物处理的适当技术,并规划运输路线。在该模型中,考虑了两个关键因素:系统运营成本和对当地居民造成的风险,并且在风险目标函数中引入了一个补偿因子,以考虑一种危险废物或处理技术所带来的风险水平可能与其他类型的风险水平有显著差异这一事实。此外,考虑了促进废物回收的政策工具,并讨论了它们对危险废物管理成本和风险的影响。该模型在Lingo优化求解器中进行编码和计算,并采用增强的ε-约束方法生成多目标优化问题的帕累托最优曲线。数值实验展示了不同目标之间的权衡。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd5d/4924005/0b77f96e8d7d/ijerph-13-00548-g001.jpg

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