A Bi-objective two-stage stochastic optimization model for sustainable reverse supply chain network design under carbon tax policy and government subsidy considering product quality

ABSTRACT

There is a growing concern over environmental pollution resulting from the production. Moreover, the expansion of various industries has led to an increased demand for raw materials. To address these challenges, this paper aims to investigate the bi-objective optimization of a sustainable reverse supply chain network while considering two key sources of uncertainty in the returned product quality and the remanufacturing capacity. Additionally, the study considers the effects of carbon tax policies and government subsidies on remanufactured products, while also focusing on three important sustainability aspects - economic, social, and environmental. The study uses two quality thresholds at inspection centers to sort products, and the epsilon constraint and NSGA-II are applied to solve the model. Through numerical analysis, the research demonstrates that objective functions are sensitive to uncertain parameters and minimum acceptable quality levels. Furthermore, the study reveals that government subsidies can offset the negative effects of carbon tax policies.

Graphical abstract

KEYWORDS:

• Reverse supply chain and sustainability
• carbon tax policy and government subsidy policy
• uncertainty in demand
• uncertainty in demand and in the capacity of remanufacturing centers
• quality control
• NSGA-II

Disclosure statement

No potential conflict of interest was reported by the authors.

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Available on request

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Notes

1. https://data.worldbank.org/indicator/EN.ATM.CO2E.PC?view=map&year=2014.

Additional information

Funding

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Notes on contributors

Mohammadreza Eslamipirharati

Mohammadreza Eslamipirharati is currently PhD student of Industrial Engineering at the School of Industrial Engineering, Sharif University of Technology. His main scientific interests include operations research, coordination, inventory control, mathematical modeling, and supply chain management.

Fariborz Jolai

Fariborz Jolai is a Professor of Industrial Engineering at the School of Industrial and Systems Engineering, College of Engineering, University of Tehran. He has published more than 300 papers in international journals, such as European Journal of Operational Research, International Journal of Production Research, International Journal of Production Economics, International Journal of Management Science and Engineering Management, Journal of Cleaner Production, Applied Mathematical Modelling, Journal of Humanitarian Logistics and Supply Chain Management, etc. His current research interests are Supply chain management, Scheduling, transportation optimization, healthcare optimization, queueing theory, supply chain management, and production planning optimization problems.

Amir Aghsami

Amir Aghsami is a Ph.D. in Industrial Engineering at the School of Industrial Engineering, Khaje Nasir Toosi University of Technology. He is currently a senior research fellow at the School of Industrial Engineering, College of Engineering, University of Tehran. He has published more than 60 papers in international journals such as Socio-Economic Planning Sciences, Computer and industrial engineering, Expert Systems with Applications, Journal of Cleaner Production, IISE Transactions on Healthcare Systems Engineering, etc. His main scientific interests include queueing theory, stochastic process, operations research, healthcare optimization, queueing-inventory systems, mathematical modeling, supply chain management, disaster management, waste management, and inventory control.