Effect of green technology for a production system through a reverse logistic process

¹ Department of Mathematics, DAV (PG) College, Maharaja Suhel Dev State University, Azamgarh 276001, Uttar Pradesh, India
² Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Sinchon-dong, Seodaemun-gu, Seoul 03722, South Korea
³ Center for Global Health Research, Saveetha Medical College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu 600077, India
⁴ Department of Mathematics, Lovely Professional University, Phagwara, Punjab 144411, India
⁵ Department of Hospitality and Tourism Management, Sejong University, 209 Neungdong-ro (Gunja-dong), Gwangjin-gu, Seoul 05006, South Korea

^* Corresponding author: bsbiswajitsarkar@gmail.com

Received: 23 April 2023
Accepted: 23 December 2023

Abstract

In modern times, customers are increasingly aware of the environmental risks posed by the premature expiration of smart products. To safeguard the environment, companies have embraced green technology when procuring products. As a result, it is challenging for business managers to capture the market by offering the best quality products at a reasonable price, regardless of the economic situation. This paper presents a production model incorporating reverse logistics to identify defective products. The model involves learning through production and utilizes green technologies. Additionally, a portion of the assembled products is remanufactured after being received from consumers. The remanufactured items are screened and distributed to markets. Both new and remanufactured products are sold to the market based on their quality in the first and second markets, respectively. To reduce product spoilage, manufacturers employ green technology like liquid cooling technology. The numerical results demonstrate that by investing in liquid cooling technology, the production store can reduce spoilage items by 8.50%, a positive environmental outcome regarding waste reduction, and due to the learning effect, the total cost can decrease by 1.44%. The paper includes numerical and sensitivity analyses accompanied by graphs.