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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Shrnutí: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 like machine scheduling problem. In this paper, we proposed an efficient optimization algorithm based on Deep RL for solving permutation flow-shop scheduling problem (PFSP) to minimize the maximum completion time. Firstly, a new deep neural network (PFSPNet) is designed for the PFSP to achieve the end-to-end output without limitation of problem sizes. Secondly, an actor-critic method of RL is used to train the PFSPNet without depending on the collection of high-quality labelled data. Thirdly, an improvement strategy is designed to refine the solution provided by the PFSPNet. Simulation results and statistical comparison show that the proposed optimization algorithm based on deep RL can obtain better results than the existing heuristics in similar computational time for solving the PFSP.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2471-285X
2471-285X
DOI:10.1109/TETCI.2021.3098354