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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Abstract	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.
AbstractList	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.
Author	Lu, Jiawen Wang, Jingjing Pan, Zixiao Wang, Ling
Author_xml	– sequence: 1 givenname: Zixiao orcidid: 0000-0001-5153-5564 surname: Pan fullname: Pan, Zixiao email: pzx19@mails.tsinghua.edu.cn organization: Department of Automation, Tsinghua University, Beijing, China – sequence: 2 givenname: Ling orcidid: 0000-0003-1226-2801 surname: Wang fullname: Wang, Ling email: wangling@mail.tsinghua.edu.cn organization: Department of Automation, Tsinghua University, Beijing, China – sequence: 3 givenname: Jingjing orcidid: 0000-0003-3385-7572 surname: Wang fullname: Wang, Jingjing organization: Department of Automation, Tsinghua University, Beijing, China – sequence: 4 givenname: Jiawen orcidid: 0000-0002-1059-4522 surname: Lu fullname: Lu, Jiawen email: jiawen.lu@huawei.com organization: Huawei Noah's Ark Lab, Shenzhen, China
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SubjectTerms	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
Title	Deep Reinforcement Learning Based Optimization Algorithm for Permutation Flow-Shop Scheduling
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