An Approach Utilizing Epsilon-Constraint and NSGA-II for Circular Manufacturing Supply Chain Networks

محورهای موضوعی : Energy saving and waste reduction in manufacturing processes

Fatemeh Jaferi ¹ ( Department of Industrial Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran )
Arash Shahin ² ( Department of Management, University of Isfahan, Isfahan, Iran )
Mohammadreza Vasili ³ ( Department of Industrial Engineering, Lenjan Branch, Islamic Azad University, Isfahan, Iran )
Omid Boyer Hassani ⁴ ( Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran )

تاریخ دریافت : 1402/02/29 تاریخ پذیرش : 1402/05/12 تاریخ انتشار : 1402/02/11

کلید واژه: NSGA-II algorithm, Optimization, epsilon-constraint, Circular Manufacturing Supply Chain,

چکیده مقاله :

Circular manufacturing supply chains offer a novel and compelling perspective within the realm of supply chain sustainability. Consequently, the development of a suitable solution approach for circular manufacturing supply chains holds significant value. This study presents appropriate solution approaches for a mathematical model that has been formulated for a circular supply chain. To address the small-sized problem, the epsilon-constraint method is proposed. This method aids in obtaining a Pareto set of optimal solutions, facilitating the evaluation of trade-offs among three objectives. Given the NP-hard nature of the problem, the non-dominated sorting genetic algorithm (NSGA-II) is employed to approximate the Pareto front for larger problem sizes. A comparative analysis is conducted between the outcomes achieved in smaller dimensions using the epsilon-constraint method and those generated by the metaheuristic algorithm. The results indicate that the error percentage of the objective function, when compared to the epsilon method, remains consistently below 1%, underscoring the effectiveness of the proposed algorithm. These methodologies empower decision-makers to offer efficient, optimal solutions, enabling them to select the most suitable alternative based on budgetary considerations and organizational policies.

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