期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 17, 期 3, 页码 1648-1657出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.3003903
关键词
Maintenance engineering; Inspection; Image restoration; Informatics; Unmanned aerial vehicles; Job shop scheduling; Schedules; Cooperative scheduling; evolutionary algorithms (EAs); human-unmanned aerial vehicles (UAV) cooperation; transmission network restoration
类别
资金
- National Natural Science Foundation of China [61872123, U1509207, 61473263]
- Natural Science Foundation of Zhejiang Province, China [LR20F030002]
The article introduces a cooperative evolutionary algorithm that simultaneously optimizes UAV scheduling solutions and human-team scheduling solutions, making them collaborate to enhance the efficiency of power supply restoration.
Power transmission networks are vulnerable to natural or man-made disasters, and it is of critical importance to efficiently restore damaged power supply in disaster-affected areas. A large-scale damaged transmission network can contain many faults that are initially uninspected/unlocated. Using unmanned aerial vehicles (UAVs) to inspect these faults can significantly improve the efficiency of subsequent restoration performed by human operators. Such a cooperative human-UAV scheduling problem is highly complex due to the correlation between UAV schedules and human-team schedules. In this article, we propose a cooperative evolutionary algorithm that simultaneously evolves two populations, one of UAV scheduling solutions (U-solutions) and the other of human-team scheduling solutions (H-solutions), which cooperate by determining a best matching U-solution for each H-solution and evaluating U-solutions based on a surrogate objective function that is iteratively improved by feedback from H-solutions. Our algorithm exhibits significant performance advantages over the state-of-the-arts on various test instances and an application to transmission network restoration in the 2017 Jiuzhaigou earthquake.
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