A GRASP approach for the energy-minimizing Electric Vehicle Routing Problem with DronesA GRASP approach for the energy-minimizing Electric Vehicle Routing Problem with Drones Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2025-02-242023enThis study addresses the Electric Vehicle Routing Problem with Drones (EVRPD) by implementing and comparing two variants of the Greedy Randomized Adaptive Search Procedure (GRASP). The primary objective of the EVRPD is to optimize the routing of a combined fleet of ground and aerial vehicles, with the aim of improving delivery efficiency and minimizing energy consumption, which is directly influenced by the weight of the packages. The study assumes a standardized packing system consisting of three weight classes, where deliveries are exclusively performed by drones, while ground vehicles function as mobile depots. The two employed GRASP variants vary in their methods of generating the Restricted Candidate List (RCL), with one utilizing a cardinality-based RCL and the other adopting a value-based RCL. To evaluate their performance, benchmark instances from the existing EVRPD literature are utilized, extensive computational experiments are conducted, and the obtained computational results are compared and discussed. The findings of the research highlight the considerable impact of RCL generation strategies on solution quality. Lastly, the study reports four new best-known values.http://creativecommons.org/licenses/by/4.0/World Electric Vehicle Journal1412Kyriakakis_et_al_World Electr. Veh. J._14(12)_2023.pdfChania [Greece]Library of TUC2025-02-24application/pdf770.3 kBfree Kyriakakis Nikolaos-Antonios Κυριακακης Νικολαος-Αντωνιος Stamadianos Themistoklis Σταμαδιανος Θεμιστοκλης Marinaki Magdalini Μαρινακη Μαγδαληνη Marinakis Ioannis Μαρινακης Ιωαννης MDPI Drones Electric vehicle Unmanned aerial vehicle routing GRASP