Estimation of fuel consumption and selection of the most carbon-efficient route for cold-chain logistics

Abstract

An eco-friendly supply chain (SC) is greatly determined by travel economy and fuel consumption rate. This research considered these two critical factors and explored the relationship between travel economy and vehicle loads, and developed mathematical models in cold-chain logistics (MMCCL) to determine fuel consumption and carbon footprint in cold-chain logistics. Longer routes are more prone to degrade food quality and endanger environmental safety by producing more carbon contents. Considering this fact, we aimed to reduce carbon emissions while maintaining food quality. First, an empirical SC was divided into three possible routes, namely, single-route transportation (SRT), and multiple-route transportation (MRT-I and MRT-II). Later, the proposed MMCCL model was deployed on each route to determine the most carbon-efficient route and found SRT as more fuel-efficient than MRT-I and MRT-II by a margin of 64.52% and 12.78%, respectively. This resulted in the reduction of carbon footprint by cutting the fuel consumption by a significant amount while making the SC eco-friendly and safe. The results were thoroughly justified and evaluated with an appropriate case study in the context of the west southern part of Bangladesh.

KEYWORDS:

• Carbon footprint
• cold-chain logistics
• environmental safety
• fuel consumption
• vehicle-routing problems

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.

Additional information

Notes on contributors

Md. Habibur Rahman

Md. Habibur Rahman is currently serving as an Assistant Professor in the Department of Industrial Engineering and Management at Khulna University of Engineering & Technology (KUET), Bangladesh. His main research interests are in the field of Supply Chain Management, Logistics and Transportation, Location Analysis, Circular Economy, and Machine Learning. Along with teaching and research, he also serves as a peer reviewer in some prestigious journals such as Production and Manufacturing Research, Production Planning and Control, International Journal of Information Technology & Decision Making.

Md. Fashiar Rahman

Md. Fashiar Rahman is a Research Assistant Professor with the Industrial, Manufacturing and Systems Engineering (IMSE) Department at the University of Texas at El Paso, USA. He received his PhD, and MS degrees in Computational Science in 2021 and 2018, respectively. He has worked on several projects in the field of image data mining, machine learning, and deep learning for industrial and healthcare applications. His research area covers advanced quality technology, AI application in health care, smart manufacturing, and computational intelligence/data analytics.

Tzu-Liang (Bill) Tseng

Tzu-Liang (Bill) Tseng is the Chair and Professor of the Industrial, Manufacturing and Systems Engineering Department at the University of Texas at El Paso, USA. He received his MS degree in Industrial Engineering (concentration on manufacturing systems and operation research/decision sciences) from the University of Wisconsin at Madison in 1993 and 1995, respectively, and a PhD in Industrial Engineering from the University of Iowa in 1999. Dr Tseng is also a Certified Manufacturing Engineer from the Society of Manufacturing Engineers since 2002. Dr Tseng is specialised in remote collaborative product design, manufacturing process, data mining, and knowledge management, specifically in the area of Internet-Based Decision Support System (IBDSS).