TY - JOUR

T1 - Прикладная задача маршрутизации для парка беспилотных летательных аппаратов с использованием модифицированного параллельного генетического алгоритма

AU - Маркелова, Анастасия Юрьевна

AU - Аллахвердян, Александр Львович

AU - Мартемьянов, Алексей Алексеевич

AU - Соколова, Инга Сергеевна

AU - Петросян, Ованес Леонович

AU - Свиркин, Михаил Владимирович

N1 - Funding Information: ∗ This work was carried out under the auspices of a grant of the President of the Russian Federation for state support of young Russian scientists — candidates of science (project N MK-4674.2021.1.1). ©c St Petersburg State University, 2022 Publisher Copyright: © 2022 Saint Petersburg State University. All rights reserved.

PY - 2022

Y1 - 2022

N2 - More and more experts agree that in the near future, most freight traffic will be carried out using automated systems, and of them drone delivery is considered to be the most promising. Drone delivery would benefit by independence from the limitations of transport infrastructure and road conditions and would ensure cargo delivery with rapid turnaround times, as well as a significant reduction of environmental impact. The technical capabilities of unmanned aerial vehicles improve year by year, so the task of coordinating drones and effectively planning routes is relevant and in great demand. The development of such technologies will help reduce transportation costs and improve customer service through faster delivery. This article discusses the applied routing problem for a fleet of drones with limited load capacity for the delivery of heterogeneous goods with the possibility of loading in multiple warehouses from an international optimization competition. The solution includes new approach based on a mixed dimensional parallel genetic algorithm (MDPGA) for finding rational routes for delivering goods to various customers and an assignment problem to reduce the dimension depending on the number of warehouses.

AB - More and more experts agree that in the near future, most freight traffic will be carried out using automated systems, and of them drone delivery is considered to be the most promising. Drone delivery would benefit by independence from the limitations of transport infrastructure and road conditions and would ensure cargo delivery with rapid turnaround times, as well as a significant reduction of environmental impact. The technical capabilities of unmanned aerial vehicles improve year by year, so the task of coordinating drones and effectively planning routes is relevant and in great demand. The development of such technologies will help reduce transportation costs and improve customer service through faster delivery. This article discusses the applied routing problem for a fleet of drones with limited load capacity for the delivery of heterogeneous goods with the possibility of loading in multiple warehouses from an international optimization competition. The solution includes new approach based on a mixed dimensional parallel genetic algorithm (MDPGA) for finding rational routes for delivering goods to various customers and an assignment problem to reduce the dimension depending on the number of warehouses.

KW - drone delivery

KW - genetic algorithm

KW - multi-depot

KW - multi-product

KW - multi-trip

KW - scheduling

KW - split-delivery

KW - vehicle routing problem

UR - http://www.scopus.com/inward/record.url?scp=85134187202&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/c5b3f646-1d4b-3ada-b4b2-9c863674bcd2/

U2 - 10.21638/11701/SPBU10.2022.111

DO - 10.21638/11701/SPBU10.2022.111

M3 - статья

AN - SCOPUS:85134187202

VL - 18

SP - 135

EP - 148

JO - ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. ПРИКЛАДНАЯ МАТЕМАТИКА. ИНФОРМАТИКА. ПРОЦЕССЫ УПРАВЛЕНИЯ

JF - ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. ПРИКЛАДНАЯ МАТЕМАТИКА. ИНФОРМАТИКА. ПРОЦЕССЫ УПРАВЛЕНИЯ

SN - 1811-9905

IS - 1

ER -