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ISSN Print: 2394-7500, ISSN Online: 2394-5869, CODEN: IJARPF

TCR (Google Scholar): 4.11, TCR (Crossref): 13, g-index: 90

Peer Reviewed Journal

Vol. 11, Issue 3, Part E (2025)

Optimization of Economic Production Quantity (EPQ) models with carbon emissions for sustainable supply chain management

Optimization of Economic Production Quantity (EPQ) models with carbon emissions for sustainable supply chain management

Author(s)
Amit Kumar, Dr. Ajay Singh Yadav and Dr. Dharmendra Yadav
Abstract
This study analyzes an optimization issue related to the Economic Production Quantity (EPQ) model, including carbon emissions within a sustainable supply chain framework. The supply chain comprises a retailer and a supplier, with each participant's profit function defined by revenue and cost functions that integrate economic, environmental, and social sustainability considerations, notably emphasizing carbon emissions. The model seeks to ascertain the appropriate Economic Production Quantity (EPQ) and pricing strategies for both the retailer and supplier, prioritizing the reduction of carbon emissions while maintaining economic viability. A fresh methodology for addressing this issue is presented. This paper's contribution is the integration of carbon emission reduction and EPQ optimization into a unified framework for sustainable supply chain management. A numerical example is shown using actual data from a two-echelon supply chain in the Iranian petrochemical sector. Sensitivity analysis is conducted to assess the influence of production capacity and the retailer's profit margin on the model's results. The findings indicate that the choice factors exhibit less sensitivity to production capacity, although are considerably influenced by fluctuations in the retailer's profit margin.
Pages: 324-333  |  68 Views  28 Downloads


International Journal of Applied Research
How to cite this article:
Amit Kumar, Dr. Ajay Singh Yadav, Dr. Dharmendra Yadav. Optimization of Economic Production Quantity (EPQ) models with carbon emissions for sustainable supply chain management. Int J Appl Res 2025;11(3):324-333.
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