A reinforcement learning-enhanced multi-objective iterated greedy algorithm for weeding-robot operation scheduling problems

•We propose a population-based iterated greedy algorithm enhanced with Q-learning for a multi-weeding-robots operation scheduling problem.•An problem-related IBH is designed to generate a set of initial solutions and a local search based on the high-load robot and the critical robot is proposed.•An...

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Vydáno v:	Expert systems with applications Ročník 263; s. 125760
Hlavní autoři:	Miao, Zhonghua, Guo, Hengwei, Pan, Quan-ke, Peng, Chen, Xu, Ziyu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 05.03.2025
Témata:	Iterated greedy algorithm Multi-objective optimization Operation scheduling Q-learning Smart farms Task allocation Operation scheduling Q-learning Iterated greedy algorithm Multi-objective optimization Smart farms Task allocation
ISSN:	0957-4174
On-line přístup:	Získat plný text
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