A ROBUST OPTIMIZATION MODEL FOR SUSTAINABLE AND RESILIENT CLOSED-LOOP SUPPLY CHAIN NETWORK DESIGN CONSIDERING CONDITIONAL VALUE AT RISK

One of the challenges facing supply chain designers is designing a sustainable and resilient supply chain network. The present study considers a closed-loop supply chain by taking into account sustainability, resilience, robustness, and risk aversion for the first time. The study suggests a two-stage mixed-integer linear programming model for the problem. Further, the robust counterpart model is used to handle uncertainties. Furthermore, conditional value at risk criterion in the model is considered in order to create real-life conditions. The sustainability goals addressed in the present study include minimizing the costs, CO2 emission, and energy, along with maximizing employment. In addition, effective environmental and social life-cycle evaluations are provided to assess the associated effects of the model on society, environment, and energy consumption. The model aims to answer the questions regarding the establishment of facilities and amount of transported goods between facilities. The model is implemented in a car assembler company in Iran. Based on the results, several managerial insights are offered to the decision-makers. Due to the complexity of the problem, a constraint relaxation is applied to produce quality upper and lower bounds in medium and large-scale models. Moreover, the LP-Metric method is used to merge the objectives to attain an optimal solution. The results revealed that the robust counterpart provides a better estimation of the total cost, pollution, energy consumption, and employment level compared to the basic model.

Automated summary

The study presents a two-stage mixed-integer linear programming model aiming to address the challenge of designing a sustainable and resilient supply chain network. Taking uncertainties into account, the model considers a closed-loop supply chain and uses a robust counterpart model along with the conditional value at risk criterion to create real-life conditions. The research goals include minimizing costs, CO2 emissions, and energy consumption, while maximizing employment, with environmental and social life-cycle evaluations assessing the model's impacts on society, environment, and energy consumption.