Delivery routing for a mixed fleet of conventional and electric vehicles with road restrictions

Abstract

The enforcement of stringent regulations capping carbon emissions has prompted city logistics enterprises to substitute electric vehicles (EVs) for conventional vehicles (CVs). For city logistics enterprises with a mixed fleet of CVs and EVs, this study investigates the delivery routing problem with road restrictions (DRPRR) for accessible time windows and bearing weight. We formulate the DRPRR as a mixed integer programming model to minimise the total operations cost. The model consists of the set-up cost of CVs and EVs, the diesel cost of CVs, the electricity cost of EVs, the carbon tax cost, and the penalty cost of vehicles waiting for roads to be accessible. To effectively solve the model, an adaptive large neighbourhood search (ALNS) algorithm is developed that consists of tailored destroy and repair operators with two alternative solution acceptance criteria. Numerical experiments are conducted to validate the effectiveness of the proposed model and ALNS algorithm. In small-scale instances, the ALNS with the Metropolis criterion finds the best solutions for 41 of 48 instances while maintaining a deviation of less than 2.5% from CPLEX in the remaining 7 instances, and its running time is significantly shorter than CPLEX. In large-scale instances, the ALNS with the Metropolis criterion has stronger solving ability and better stability than benchmark algorithms (i.e. GA-LS, LNS, and ALNS with a threshold acceptance criterion). We also address a real-world case and conduct a sensitivity analysis to provide useful managerial insights. Specifically, implementing road restrictions on accessible time windows and the carbon tax policy simultaneously is more appropriate from the comprehensive perspective of market activity and carbon emissions.

Keywords:

• Sustainable cities and communities
• delivery routing
• electric vehicles
• road restriction
• adaptive large neighbourhood search

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.

Notes

1 http://m.ce.cn/qc/gd/202302/06/t20230206_38377037.shtml (In Chinese, last accessed February 23, 2023)

2 https://www.jdl.com/news/2335/content00755?type=0 (In Chinese, last accessed February 20, 2023)

3 http://www.gzns.gov.cn/gznsga/gkmlpt/content/8/8516/mpost_8516044.html#10448 (In Chinese, last accessed February 20, 2023)

Additional information

Funding

This work was supported by the Beijing Social Science Foundation under Grant 22GLC059, the National Natural Science Foundation of China under Grant 71931001, the Beijing Nova Program under Grant 20230484421, the Innovation Centre for Digital Business and Capital Development of Beijing Technology and Business University under Grant SZSK202204, the Foundation of Hebei Educational Committee under Grant QN2022042, and the Funds for First-class Discipline Construction under Grant XK1802-5.

Notes on contributors

Hongguang Ma

Hongguang Ma received the Ph.D. degree in control science and engineering from Beijing University of Chemical Technology, China, in 2019. From 2018 to 2019, he was a Visiting Scholar with the J. Mack Robinson College of Business, Georgia State University, Atlanta, GA, USA. He is currently a Lecturer with the School of Economics and Management, Beijing University of Chemical Technology. He has authored more than 30 articles on international journals, including IEEE Transactions on Fuzzy Systems, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Engineering Management, Information Sciences, and Computers and Industrial Engineering. His research interests include transportation management, operations research and optimisation, and big data analysis. He has served as the Guest Editor for Information Sciences, Soft Computing, Scientific Programming, and Axioms.

Rongchao Yang

Rongchao Yang received the B.S. degree in logistics management from Hainan University, China, in 2021. He is currently pursuing the degree in technology economy and management with the Beijing University of Chemical Technology, China. His current research interests include logistics management, and modelling and optimisation.

Xiang Li

Xiang Li received the Ph.D. degree in operations research and control from Tsinghua University, Beijing, China, in 2008. He is currently a Professor with the School of Economics and Management Science, Chang'an University, China. He has authored two books and more than 130 articles on international journals, including IEEE Transactions on Fuzzy Systems, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Engineering Management, European Journal of Operational Research, Transportation Research Part B: Methodological, Transportation Research Part C: Emerging Technologies, International Journal of Production Economics, and Information Sciences. His research interests include intelligent transportation systems, optimisation under uncertainty, and big data analysis. He serves as the Editor-in-Chief of International Journal of General Systems and Journal of Data, Information and Management and an Associate Editor of Omega-The International Journal of Management Science, Information Sciences, and Transportmetrica B: Transport Dynamics.