Deep Reinforcement Learning Based Optimization Algorithm for Permutation Flow-Shop Scheduling

As a new analogy paradigm of human learning process, reinforcement learning (RL) has become an emerging topic in computational intelligence (CI). The synergy between the RL and CI is an emerging way to develop efficient solution algorithms for solving complex combinatorial optimization (CO) problems...

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Vydané v:	IEEE transactions on emerging topics in computational intelligence Ročník 7; číslo 4; s. 983 - 994
Hlavní autori:	Pan, Zixiao, Wang, Ling, Wang, Jingjing, Lu, Jiawen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Artificial neural networks Combinatorial analysis Completion time Computational intelligence Computing time Decoding Deep learning deep neural network Dynamic scheduling Encoding flow-shop scheduling improvement strategy Job shop scheduling Machine learning Optimization optimization algorithm Optimization algorithms Permutations Reinforcement learning
ISSN:	2471-285X, 2471-285X
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