Sustainable closed-loop supply chain network design under uncertainty using a fuzzy multi-objective optimization framework for the battery industry

Creative Commons License

Attari M. Y. N., Rezanejad S., Ala A., Simic V., Pamucar D.

SCIENTIFIC REPORTS, vol.16, no.1, 2026 (SCI-Expanded, Scopus)

  • Publication Type: Article / Article
  • Volume: 16 Issue: 1
  • Publication Date: 2026
  • Doi Number: 10.1038/s41598-026-47477-8
  • Journal Name: SCIENTIFIC REPORTS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Chemical Abstracts Core, EMBASE, MEDLINE, Directory of Open Access Journals, Zoological Record, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest)
  • Open Archive Collection: Digital Heritage Collection
  • Azerbaijan State University of Economics (UNEC) Affiliated: Yes

Abstract

The study presents a sustainable closed-loop supply chain network that integrates financial, environmental, and social objectives within a context of uncertainty. A fuzzy-based modeling approach is introduced to address uncertainty in customer demand, cost parameters, and carbon emission coefficients across the sustainable closed-loop supply chain network. Two metaheuristic methods, the non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO), are employed to address the problem and are compared against each other. A practical case study of a battery company is employed to validate the framework. The findings indicate that MOPSO surpasses non-dominated sorting genetic algorithm II in terms of solution quality and computational efficiency, compared with NSGA-II, the proposed MOPSO achieved a 6.3% reduction in total cost and an 8.1% decrease in CO2 emissions, while the social index reflecting recruitment and employee security increased by 12.5%. This study contributes a sustainable closed-loop supply chain network design model for the battery industry that together optimizes economic, environmental, and social objectives amid parameter uncertainty, and offers algorithmic evaluations of optimized multi-objective metaheuristics to achieve high-quality Pareto solutions.