Cleaner Logistics and Supply Chain | 2026
Authors: Jamali M.S.; Tavakkoli-Moghaddam R.; Tafakkori K.; Dolgui A.
DOI: 10.1016/j.clscn.2026.100333
Journal: Cleaner Logistics and Supply Chain
Year: 2026
Publisher: Elsevier Ltd
Document Type: Article
Open Access: All Open Access; Gold Open Access; Green Open Access
Cited by: 1
This study designs a sustainable closed-loop lime supply chain network under simulated capacity disruptions; managed resiliently with backup suppliers, redundant capacities, and multiple supply sources. A multi-objective, multi-period, and multi-product mathematical model is proposed to minimize costs and carbon dioxide emissions and maximize job opportunities. Using the best among five classifcation models, selected by weighted aggregated sum product assessment, customer groups are labeled to determine the uncertainty intervals of their demand deviations for a robust optimization method. Then, the capacity disruptions are simulated using a two-stage stochastic programming approach; in which the proactive decisions include contracting primary/backup suppliers and establishing facilities, and the reactive ones are increasing capacity levels and selecting alternative links between echelons. An augmented ε-constraint method and several meta- heuristic algorithms, including multi–objective simulated annealing, non-dominated sorting genetic algorithm, and multi–objective grey wolf optimizer, are tailored to support the solution of the model at different scales. To evaluate their efficiency, an extensive comparison is conducted using several widely used multi-objective optimization metrics. Sensitivity analyses provide practical management advice, underscoring that resilient strategies reduce costs by 28% and carbon dioxide emissions by 33%. © 2026 The Author(s).
Closed-loop supply chain; Deep uncertainty; Machine learning; Resilience; Robust-stochastic optimization; Sustainable agriculture