Multi-Objective Sustainable Closed-Loop Supply Chain Network Design Considering Multiple Products with Different Quality Levels

• Main Authors: Antucheviciene, J. (Author), Heidari, A. (Author), Khalilzadeh, M. (Author), Soon, A. (Author), Zahedi, F. (Author), Zavadskas, E.K (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Augmented Epsilon Constraint (AEC); customer consumption trend; reverse logistics; robust possibilistic programming; supply chain network design; un-certainty
• Online Access: View Fulltext in Publisher

 International laws and increasing consumer awareness have led to drastic changes in traditional supply chain network designs. Moreover, because of environmental and social require-ments, traditional supply chain networks have changed to sustainable supply chain networks. On the other hand, reverse logistics can be effective in terms of environmental and economic aspects, so the design of the supply chain network as a closed loop is necessary. In addition, customers have a demand for different products with different quality levels. Considering different types of customers with a variety of consumption trends can be a challenging issue, and is addressed in this study. The main contributions of this research are considering different quality levels for products as well as different tendencies of customers towards environmental issues. In this study, a sustainable closed-loop supply chain model is designed that seeks to balance economic, environmental, and social responsibilities. In this paper, costs and customer demands for different types of products at different quality levels are considered under uncertain conditions using a robust possibilistic programming method. The proposed multi-objective model is solved using the Augmented Epsilon Constraint (AEC) method that provides an efficient set of solutions for all decision-making levels. The results show that the robust possibilistic programming method is more effective in dealing with uncertainties than the possibilistic programming method. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.