Routing a mixed fleet of electric and conventional vehicles under regulations of carbon emissions

Abstract

This paper seeks to optimise fleet management for companies that utilise both internal combustion engine vehicles (ICEVs) and electric vehicles by considering various costs, including fixed costs, variable costs, time window penalty costs, fuel costs, and electricity consumption costs. Additionally, the study examines the influence of four types of regulations, i.e. carbon cap, carbon tax, carbon trading, and carbon offsetting, on carbon emissions and fleet configuration. The optimisation model was solved using a Clarke-Wright savings heuristic algorithm followed by an improved adaptive genetic algorithm (IAGA), and a sensitivity analysis was conducted under different regulatory policies. The results show that all four types of regulations can effectively reduce fleet emissions. While carbon prices have a greater impact on carbon regulations than carbon quota, carbon trading was more effective under similar circumstances. Therefore, governments should implement appropriate regulatory strategies to reduce energy consumption and encourage enterprises to reduce their emissions.

Keywords:

• Carbon emissions regulations
• the mixed fleet
• electric vehicles
• improved adaptive genetic algorithm
• savings heuristic

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 as indicated in the paper.

Additional information

Funding

This work is supported by the National Foreign Expert Program of the Ministry of Science and Technology [grant number G2021014038L]; the NSFC [grant number 72371135 and 71571092]; the Major Projects for Natural Science Research in Colleges and Universities of Jiangsu Province [grant number 19KJA520002]; the High-level Talent Project from ‘Six Peaks of Top Talents’ in Jiangsu Province [grant number JY-076].

Notes on contributors

Yuzhuo Qiu

Yuzhuo Qiu was born in Nei Mongol, China in 1974. She serves as Professor and Dean of the School of Business at the Nanjing University of Information Science & Technology (NUIST), China. Additionally, she is the Executive Dean of the Institute of Climate Economy and Low-Carbon Industry, NUIST. She is engaged in education, teaching, and scientific research in the fields of supply chain resilience and security, low-carbon and smart supply chain operations management, and logistics system optimisation. She has presided over a number of national, provincial and ministerial projects, such as the general project of the National Natural Science Foundation of China, and the key project of Jiangsu Social Science Foundation. She has published more than 30 papers in the journals such as Omega, Transportation Research Part E, etc., and her papers have been selected as ESI hot papers and highly cited papers.

Shu Ding

Shu Ding was born in Jiangsu, China in 1997. She is currently pursuing a Master of Business Administration degree in the School of Business, Nanjing University of Information Science & Technology, Nanjing, China. Her main research direction is the vehicle routing problem and logistics management.

Panos M. Pardalos

Panos M. Pardalos was born in Greece in 1954. Panos M. Pardalos serves as professor emeritus of industrial and systems engineering at the University of Florida. Additionally, he is the Paul and Heidi Brown Preeminent Professor of industrial and systems engineering. He is also an affiliated faculty member of the computer and information science Department, the Hellenic Studies Center, and the biomedical engineering programme. He is also the director of the Center for Applied Optimization. Pardalos is a world leading expert in global and combinatorial optimisation. His recent research interests include network design problems, optimisation in telecommunications, e-commerce, data mining, biomedical applications, and massive computing.