Solving electric vehicle–drone routing problem using memetic algorithm

This paper considers a cooperative system between electric vehicles and drones in last-mile logistics operations. This form of cooperation is presented as electric vehicle–drone routing problem (EVDRP). In EVDRP, a set of electric vehicles are equipped with drones to deliver parcels to a set of cust...

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Veröffentlicht in:Swarm and evolutionary computation Jg. 79; S. 101295
Hauptverfasser: Mara, Setyo Tri Windras, Sarker, Ruhul, Essam, Daryl, Elsayed, Saber
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.06.2023
Schlagworte:
Electric vehicles
Future transportation systems
Last-mile logistics
Memetic algorithm
Unmanned aerial vehicles
Vehicle routing problem with drones
Memetic algorithm
Electric vehicles
Last-mile logistics
Vehicle routing problem with drones
Unmanned aerial vehicles
Future transportation systems
ISSN:2210-6502
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Zusammenfassung:This paper considers a cooperative system between electric vehicles and drones in last-mile logistics operations. This form of cooperation is presented as electric vehicle–drone routing problem (EVDRP). In EVDRP, a set of electric vehicles are equipped with drones to deliver parcels to a set of customer nodes. Due to the battery limitation of electric vehicles, a set of available recharging stations is considered to recharge the vehicles’ energy and/or to launch and retrieve drones. In this research, a mixed-integer programming model is formulated for EVDRP to minimize the total completion time. To solve the model, a memetic algorithm-based approach with four new problem-specific operators is developed. From comprehensive numerical experiments, the effectiveness of the algorithm is shown, and several useful managerial insights are derived. •An integrated logistics system with electric vehicles and drones is considered.•A mixed-integer programming formulation is presented.•A memetic algorithm with four new problem-specific operator is proposed.•Managerial discussions are presented based on the results.
ISSN:2210-6502
DOI:10.1016/j.swevo.2023.101295