Robust optimization of risk-aware, resilient and sustainable closed-loop supply chain network design with Lagrange relaxation and fix-and-optimize

This study explores a Robust, Risk-aware, Resilient, and Sustainable Closed-Loop Supply Chain Network Design (3RSCLSCND) to tackle demand fluctuation like COVID-19 pandemic. A two-stage robust stochastic multiobjective programming model serves to express the proposed problems in formulae. The object...

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Vydáno v:International journal of logistics Ročník 27; číslo 5; s. 705 - 745
Hlavní autoři: Lotfi, Reza, Sheikhi, Zohre, Amra, Mohsen, AliBakhshi, Mehdi, Weber, Gerhard-Wilhelm
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 03.05.2024
Taylor & Francis LLC
Témata:
Algorithms
Closed loops
Closed-loop supply chain
Design optimization
Energy consumption
Energy costs
fix-and-optimize
Lagrangian relaxation
Mathematical programming
Minimax technique
Multiple objective analysis
Network design
resiliency
risk
Risk management
Robustness
Supply chains
sustainability
Upper bounds
ISSN:1367-5567, 1469-848X
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Shrnutí:This study explores a Robust, Risk-aware, Resilient, and Sustainable Closed-Loop Supply Chain Network Design (3RSCLSCND) to tackle demand fluctuation like COVID-19 pandemic. A two-stage robust stochastic multiobjective programming model serves to express the proposed problems in formulae. The objective functions include minimising costs, CO 2 emissions, energy consumption, and maximising employment by applying Conditional Value at Risk (CVaR) to achieve reliability through risk reduction. The Entropic Value at Risk (EVaR) and Minimax method are used to compare with the proposed model. We utilise the Lp-Metric method to solve the multiobjective problem. Since this model is complex, the Lagrange relaxation and Fix-and-Optimise algorithm are applied to find lower and upper bounds in large-scale, respectively. The results confirm the superior power of the model offered in estimating costs, energy consumption, environmental pollution, and employment level. This model and algorithms are applicable for other CLSC problems.
Bibliografie:ObjectType-Article-1
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ISSN:1367-5567
1469-848X
DOI:10.1080/13675567.2021.2017418