A multi-objective approach for designing a tire closed-loop supply chain network considering producer responsibility

•Developing a new optimization model for designing a tire closed-loop supply chain.•Presenting a Spherical fuzzy logic method to calculate the importance of suppliers.•Discussing the application of the model in Greater Toronto Area, Canada.•Solving the multi-objective model using the augmented ε-con...

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Veröffentlicht in:	Applied mathematical modelling Jg. 115; S. 616 - 644
Hauptverfasser:	Ahmed, Javeria, Amin, Saman Hassanzadeh, Fang, Liping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.03.2023
Schlagworte:	Augmented epsilon constraint method Multi-objective mixed-integer linear programming Producer responsibility Spherical fuzzy logic Tire closed-loop supply chain Tire closed-loop supply chain Multi-objective mixed-integer linear programming Augmented epsilon constraint method Producer responsibility Spherical fuzzy logic
ISSN:	0307-904X
Online-Zugang:	Volltext
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Zusammenfassung:	•Developing a new optimization model for designing a tire closed-loop supply chain.•Presenting a Spherical fuzzy logic method to calculate the importance of suppliers.•Discussing the application of the model in Greater Toronto Area, Canada.•Solving the multi-objective model using the augmented ε-constraint method.•Performing extensive sensitivity analyses and analyzing the results. For tire manufacturers to remain profitable while fulfilling environmental and social obligations such as producer responsibility, the opportunity lies in designing a tire Closed-Loop Supply Chain (CLSC) which combines forward and reverse supply chains. In this paper, a new multi-objective mixed-integer linear programming model is proposed to configure and optimize a multi-echelon, multi-product, multi-period tire CLSC network based on multiple recovery options and markets. For one of the objectives of the model, the weighting factors (importance) of suppliers are determined according to a unique framework of qualitative criteria. In this respect, a novel decision-making method based on Spherical fuzzy logic is developed. Finally, the solution approach is devised based on the formulation of the augmented ε-constraint method for finding efficient solutions. The application of the model is illustrated focusing on the region of Greater Toronto Area in Ontario, Canada. The optimal quantities for the flows of products, and number and locations of open facilities of the network are computed. The results show that the selected suppliers and allocated orders from them are impacted by considering multiple objectives.
ISSN:	0307-904X
DOI:	10.1016/j.apm.2022.10.028