Multi-objective stochastic closed-loop supply chain network design with social considerations

• Published in: Applied soft computing Vol. 71; pp. 505 - 525
• Main Authors: Fathollahi-Fard, Amir Mohammad, Hajiaghaei-Keshteli, Mostafa, Mirjalili, Seyedali
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2018
• Subjects: Closed-loop supply chain; Hybrid metaheuristic approaches; Metaheuristics; Social considerations; Stochastic programming; Social considerations; Metaheuristics; Hybrid metaheuristic approaches; Closed-loop supply chain; Stochastic programming
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2018.07.025

The graphical structure of a simple closed-loop supply chain network utilizing in this paper. [Display omitted] •Introducing a new multi-objective stochastic closed-loop supply chain network design with social considerations.•Presenting a number of new hybrid metaheuristic algorithms to create a better interaction between the search phases.•Comparison of developed approaches by a set of efficient evaluation metrics of Pareto optimal sets.•Confirming the performance of proposed RDKAGA in practice. Nowadays, operation managers usually need efficient supply chain networks including important design factors such as economic and social considerations. The recent decade has seen a rapid development of controlling the uncertainty in supply chain configurations along with proposing novel solution approaches. By investigating the related studies, this paper shows that most of the current studies consider the economic aspects and just a few works present the two-stage stochastic programming as well as social considerations to design a closed-loop supply chain network. This motivated our attempts to consider economic and social aspects simultaneously by using the mentioned suppositions among the first studies. Another main contribution of this paper is the hybridization and tuning of a number of recent algorithms to address the problem. The results show that the proposed hybrid metaheuristic algorithms outperform the best existing techniques on the majority of case studies.