A Simulated Annealing Heuristic Approach for the Energy Minimizing Electric Vehicle Routing Problem with Drones

Nikolaos A. Kyriakakis (), Themistoklis Stamadianos (), Magdalene Marinaki () and Yannis Marinakis ()
Additional contact information
Nikolaos A. Kyriakakis: Technical University of Crete
Themistoklis Stamadianos: Technical University of Crete
Magdalene Marinaki: Technical University of Crete
Yannis Marinakis: Technical University of Crete

A chapter in Disruptive Technologies and Optimization Towards Industry 4.0 Logistics, 2024, pp 227-246 from Springer

Abstract: Abstract The Electric Vehicle Routing Problem with Drones (EVRPD) is a recently proposed VRP that combines two state-of-the-art means of transportation, electric ground vehicles (EVs) and drones, intending to minimize total energy consumption. The payload weight is considered the element that has the greatest impact on the energy consumption rate. The EVRPD assumes packages of different weight classes. The EVs serve as mobile depots, from which drones are deployed to deliver the packages. Both vehicle types have quantity, weight, and energy limitations. The Simulated Annealing heuristic of this research follows a population-based approach, which utilizes neighborhood operators to evolve the solutions. Three different temperature decaying strategies are tested on the EVRPD benchmark instances found in the literature, and their computational results are compared and discussed.

Keywords: Electric vehicles; Simulated annealing; Unmanned aerial vehicles; Multiple trips (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-58919-5_8

Ordering information: This item can be ordered from
http://www.springer.com/9783031589195

DOI: 10.1007/978-3-031-58919-5_8

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().