Risk-averse distributionally robust optimization for construction waste reverse logistics with a joint chance constraint

IF 4.1 2区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0

Abstract

The uncertainty of the amount of construction and demolition waste (CDW) generation affects the CDW reverse logistics network service level. We investigate a CDW reverse logistics network location-routing problem considering uncertainties. To minimize the total social cost, a two-stage risk-averse distributionally robust optimization model is developed, which aims to optimize the location and number of CDW disposal facilities and the CDW transportation scheme. We introduce the mean-conditional value at risk measure and a joint chance constraint into our model to consider the government’s risk aversion. The above model is approximated as a standard second-order cone programming model (SOCP) considering a special case. To exactly solve the SOCP, we design an outer approximation algorithm. Several performance tests and a case study are proposed, and sensitivity analysis provides helpful managerial insights.

具有联合机会约束的建筑垃圾逆向物流的风险规避分布稳健优化
建筑和拆除垃圾(CDW)产生量的不确定性会影响 CDW 逆向物流网络的服务水平。我们研究了一个考虑到不确定性的拆建垃圾逆向物流网络选址问题。为了使社会总成本最小化,我们建立了一个两阶段风险规避分布稳健优化模型,旨在优化 CDW 处置设施的位置和数量以及 CDW 运输方案。为了考虑政府的风险规避,我们在模型中引入了平均条件风险值和联合机会约束。考虑到特殊情况,上述模型近似为标准二阶圆锥编程模型(SOCP)。为了精确求解 SOCP,我们设计了一种外近似算法。我们提出了几个性能测试和一个案例研究,并通过敏感性分析提供了有用的管理见解。
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来源期刊
Computers & Operations Research
Computers & Operations Research 工程技术-工程:工业
CiteScore
8.60
自引率
8.70%
发文量
292
审稿时长
8.5 months
期刊介绍: Operations research and computers meet in a large number of scientific fields, many of which are of vital current concern to our troubled society. These include, among others, ecology, transportation, safety, reliability, urban planning, economics, inventory control, investment strategy and logistics (including reverse logistics). Computers & Operations Research provides an international forum for the application of computers and operations research techniques to problems in these and related fields.
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