Abstract
In recent years, there are many research achievements in the Unmanned Aerial Vehicle (UAV) fields. UAV can be used to deliver the logistics and do surveillance as well. Two main problems in this field are UAV-routing and UAV-scheduling. In this paper, we focus on the UAV scheduling problem, which is the problem to search for the scheduling order of the UAV using a fixed number of UAVs and a fixed number of targets. The objective of this paper is to minimize the total cost for efficient realization. A Genetic Algorithm (GA) method is used to solve the UAV-scheduling problem considering the time-varying cost or Time-of-Use tariff (ToU) constraints. The Job Delay Mechanism is also used to improve cost optimization, as a kind of post-processing for the fitness evaluation of an individual schedule, and show that GA alone can not find it. Finally, a numerical experiment is conducted to implement the idea in this paper. Experiment results showed that the proposed method is quite promising and effective in solving the related problem.
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Mantau, A.J., Widayat, I.W., Köppen, M. (2022). A Genetic Algorithm for Parallel Unmanned Aerial Vehicle Scheduling: A Cost Minimization Approach. In: Barolli, L., Chen, HC., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2021. Lecture Notes in Networks and Systems, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-84910-8_14
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DOI: https://doi.org/10.1007/978-3-030-84910-8_14
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