Open AccessAn enhanced genetic algorithm for unmanned aerial vehicle logistics scheduling
Author(s) -
Yuan Xiaoxiang,
Zhu Jie,
Li Yixuan,
Huang Haiping,
Wu Min
Publication year - 2021
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/cmu2.12106
Subject(s) - computer science , scheduling (production processes) , vehicle routing problem , job shop scheduling , mathematical optimization , genetic algorithm , population , real time computing , routing (electronic design automation) , mathematics , computer network , machine learning , demography , sociology
This paper examines a scheduling problem with heterogeneous logistics unmanned aerial vehicles (UAVs) in urban environment. Different from traditional vehicle routing problem (VRP), it introduces some new characteristics such as the loading capacity, the maximum flight time and the flight speed. As a variant of VRP, the considered scheduling problem is known to be an non‐deterministic Polynomial (NP)‐hard problem. The UAV scheduling problem model with the heterogeneous UAV settings is formulated first. Secondly, a genetic‐based algorithm framework is presented for solving the scheduling problem, in which the encoding/decoding method, the initial population generation method and genetic operations are delicately designed. In order to reduce the search space and faster the execution of this algorithm, a weight‐based loading method is adopted. For the purpose of performance evaluation and statistical analysis, the proposed algorithm is compared with the other two existing algorithms. The experimental results show that the presented algorithm can solve this problem efficiently.