CVAR公司
数学优化
计算机科学
供应链网络
放松(心理学)
极小极大
拉格朗日松弛
供应链
预期短缺
可靠性(半导体)
随机规划
风险管理
供应链管理
功率(物理)
数学
经济
量子力学
社会心理学
物理
管理
法学
政治学
心理学
作者
Reza Lotfi,Zohre Sheikhi,Mohsen Amra,Mehdi Alibakhshi,Gerhard‐Wilhelm Weber
标识
DOI:10.1080/13675567.2021.2017418
摘要
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, CO2 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.
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