A comprehensive bi-objective optimization model to design circular supply chain networks for sustainable electric vehicle batteries

Afshin Meraj; Tina Shoa; Fereshteh Sadeghi Naieni Fard; Hassan Mina; Afshin Meraj; Tina Shoa; Fereshteh Sadeghi Naieni Fard; Hassan Mina

doi:10.3934/environsci.2024013

AIMS Environmental Science

2024, Volume 11, Issue 2: 279-303. doi: 10.3934/environsci.2024013

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A comprehensive bi-objective optimization model to design circular supply chain networks for sustainable electric vehicle batteries

1.
College of Engineering and Computing Sciences, Vancouver Campus, New York Institute of Technology, Vancouver, Canada
2.
Department of information Science, University of North Texas, Denton, TX76207, USA
3.
Prime School of Logistics, Saito University College, Petaling Jaya, Selangor, Malaysia

Received: 24 January 2024 Revised: 31 March 2024 Accepted: 24 April 2024 Published: 28 April 2024

As electric vehicles (EVs) continue to advance, there is a growing emphasis on sustainability, particularly in the area of effectively managing the lifecycle of EV batteries. In this study, an efficient and novel optimization model was proposed for designing a circular supply chain network for EV batteries. In doing so, a comprehensive, bi-objective, mixed-integer linear programming model was employed. It is worth noting that the current model outlined in this paper involved both forward and reverse flows, illustrating the process of converting used batteries into their constituent materials or repurposing them for various applications. In line with the circular economy concept, the current model also minimized the total costs and carbon emission to develop an inclusive optimization framework. The LP-metric method was applied to solve the presented bi-objective optimization model. We simulated six problems with different sizes using data and experts' knowledge of a lithium-ion battery manufacturing industry in Canada, and evaluated the performance of the proposed model by simulated data. The results of the sensitivity analysis process of the objective functions coefficients showed that there was a balance between the two objective functions, and the costs should be increased to achieve lower emissions. In addition, the demand sensitivity analysis revealed that the increase in demand directly affects the increase in costs and emissions.
- circular economy,
- sustainable supply chain,
- electric vehicle battery,
- optimization,
- LP-metric method
Citation: Afshin Meraj, Tina Shoa, Fereshteh Sadeghi Naieni Fard, Hassan Mina. A comprehensive bi-objective optimization model to design circular supply chain networks for sustainable electric vehicle batteries[J]. AIMS Environmental Science, 2024, 11(2): 279-303. doi: 10.3934/environsci.2024013

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Abstract

As electric vehicles (EVs) continue to advance, there is a growing emphasis on sustainability, particularly in the area of effectively managing the lifecycle of EV batteries. In this study, an efficient and novel optimization model was proposed for designing a circular supply chain network for EV batteries. In doing so, a comprehensive, bi-objective, mixed-integer linear programming model was employed. It is worth noting that the current model outlined in this paper involved both forward and reverse flows, illustrating the process of converting used batteries into their constituent materials or repurposing them for various applications. In line with the circular economy concept, the current model also minimized the total costs and carbon emission to develop an inclusive optimization framework. The LP-metric method was applied to solve the presented bi-objective optimization model. We simulated six problems with different sizes using data and experts' knowledge of a lithium-ion battery manufacturing industry in Canada, and evaluated the performance of the proposed model by simulated data. The results of the sensitivity analysis process of the objective functions coefficients showed that there was a balance between the two objective functions, and the costs should be increased to achieve lower emissions. In addition, the demand sensitivity analysis revealed that the increase in demand directly affects the increase in costs and emissions.

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