Routing a mixed fleet of conventional and electric vehicles for urban delivery problems: considering different charging technologies and battery swapping

Abstract

We present a vehicle routing problem with load capacity and time windows for a fleet of electric vehicles (EVs) and internal combustion vehicles (ICVs). Different charging technologies, including Level 1, 2, and 3 chargers and swapping batteries, are considered in this research. Given the location of the depot, the existing customers, and the set of charging stations, this problem aims to minimise the overall cost of constructing the routes over the vertices that need to be visited by either an ICV or EV. We develop a mixed-integer linear programming (MILP) model for this problem, and we solve small samples using a CPLEX solver. In addition, we develop two metaheuristic solution approaches by combining Adaptive Large Neighbourhood Search (ALNS) with Simulated Annealing (SA) and Tabu Search (TS). Using a set of locations from Scarborough, Ontario, Canada, we investigate the delivery routing problem with a fleet of ICVs and EVs. By solving the problem for different scenarios, we observed that EVs often require partial recharging and faster chargers (Level 3) when traveling in the city.

KEYWORDS:

• Electric vehicle routing problem
• mixed-integer linear programming
• mixed-fleet
• battery swapping
• metaheuristics

Acknowledgements

This work was supported by Discovery Grants from the Natural Sciences and Engineering Research Council of Canada (grant # RGPIN-2017-04434 and grant # RGPIN-2017-04481).

Data availability

The data that support the findings of this study are openly available via Dropbox at https://www.dropbox.com/s/43aju7bhkhyssit/Samples.xlsx?dl=0.

Disclosure statement

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

Additional information

Funding

This work was supported by Natural Sciences and Engineering Research Council of Canada: [Grant Number RGPIN-2017-04434,RGPIN-2017-04481].

Notes on contributors

Afsane Amiri

Dr. Afsane Amiri earned her PhD in Industrial Engineering from Toronto Metropolitan University, where she specialized in optimization, supply chain management, transportation, and logistics. With her expertise in these areas, Afsane applies her knowledge to effectively solve problems and provide solutions.

Hossein Zolfagharinia

Dr. Hossein Zolfagharinia is an Associate Professor of Operations Management in the Global Management Studies department at the Ted Rogers School of Management, Toronto Metropolitan University. He received his Undergraduate and Master's Degrees in Industrial Engineering. Afterwards, he earned his Ph.D. in Operations and Supply Chain Management from the Lazaridis School of Business and Economics at Wilfrid Laurier University. He is the co-editor of a book and the author of a book chapter and over 20 top-tier refereed articles in A and A* rated journals, based on the ABDC list.

Saman Hassanzadeh Amin

Dr. Saman Hassanzadeh Amin is an Associate Professor at the Department of Mechanical and Industrial Engineering at Toronto Metropolitan University (TMU). Prior to joining TMU, he was an Assistant Professor of Supply Chain Management at Cape Breton University. Dr. Amin’s research expertise spans disciplines and includes Supply Chain Management, Operations Management, Operations Research, Optimization, Information Technology, and Decision Support Systems. He has published more than 47 articles in well-known journals.