Optimization of a resilient circular closed-loop supply chain network under uncertainty

Lubricating oils are among the most widely used petroleum fractions since they are used by different machines and vehicles. In addition to cooling the engine and reducing the friction between moving mechanical parts, motor oil also absorbs pollutants such as sludge, peroxides, and debris that are ac...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Cleaner Engineering and Technology Jg. 27; S. 100995
Hauptverfasser: Mehrabi, Samira, Mina, Hassan, Sorooshian, Shahryar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.07.2025
Elsevier
Schlagworte:
approach
Business Administration
Circular economy
Fuzzy goal programming method
Företagsekonomi
Optimization model
Resilient supply chain
Scenario-based stochastic programming
Scenario-based stochastic programming approach
Circular economy
Fuzzy goal programming method
Optimization model
Scenario-based stochastic programming approach
Resilient supply chain
ISSN:2666-7908, 2666-7908
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Lubricating oils are among the most widely used petroleum fractions since they are used by different machines and vehicles. In addition to cooling the engine and reducing the friction between moving mechanical parts, motor oil also absorbs pollutants such as sludge, peroxides, and debris that are accumulated in the engine. Therefore, used motor oils are the dangerous materials that can have adverse impacts on the environment and living organisms if not properly managed. Hence, for the first time, this article develops a new bi-objective mixed-integer linear programming (BOMILP) model to form a resilient circular closed-loop supply chain network for managing the used motor oils. Moreover, with the aim of achieving advanced sustainability and circularity goals, the proposed model manages collecting, recycling, producing, and purchasing gallons. Moreover, this study applies a scenario-based stochastic programming method to control the demand uncertainty, and provides a novel fuzzy goal programming method to solve the developed BOMILP model. Finally, the data of an Iranian motor oil production company is applied to validate the proposed optimization model and evaluate the performance of the presented multi-objective solution approach. The results derived from implementing the developed optimization model in the real world and conducting the sensitivity analysis process denote the effectiveness and accuracy of the developed optimization model and solution method.
ISSN:2666-7908
2666-7908
DOI:10.1016/j.clet.2025.100995