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

•We investigated a CDW reverse logistics network.•A two-stage risk-averse distributionally robust optimization model is established.•The M-CVaR measure and a joint chance constraint are introduced.•We design an outer approximation algorithm to obtain the solution.•A case study on the CDW reverse net...

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Bibliographic Details
Published in:	Computers & operations research Vol. 173; p. 106829
Main Authors:	Xin, Xu, Zhang, Tao, Wang, Xiaoli, He, Fang, Wu, Lingxiao
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.01.2025
Subjects:	Construction and demolition waste (CDW) Distributionally robust optimization (DRO) Joint chance constraint Location-routing problem (LRP) Mean-conditional value at risk (M-CVaR) Outer approximation algorithm (OAA) Distributionally robust optimization (DRO) Joint chance constraint Mean-conditional value at risk (M-CVaR) Location-routing problem (LRP) Construction and demolition waste (CDW) Outer approximation algorithm (OAA)
ISSN:	0305-0548
Online Access:	Get full text
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Summary:	•We investigated a CDW reverse logistics network.•A two-stage risk-averse distributionally robust optimization model is established.•The M-CVaR measure and a joint chance constraint are introduced.•We design an outer approximation algorithm to obtain the solution.•A case study on the CDW reverse network design is performed. 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.
ISSN:	0305-0548
DOI:	10.1016/j.cor.2024.106829