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Solving non-permutation flow-shop scheduling problem via a novel deep reinforcement learning approach.

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Title: Solving non-permutation flow-shop scheduling problem via a novel deep reinforcement learning approach.
Authors: Wang, Zhenyu1, zhenyuwang@cqu.edu.cn, Cai, Bin1, caibin@cqu.edu.cn, Li, Jun1, li.jun@cqu.edu.cn, Yang, Deheng2, yangdeheng13@nudt.edu.cn, Zhao, Yang1, zhaoyang1@cqu.edu.cn, Xie, Huan1, huanxie@cqu.edu.cn
Source: Computers & Operations Research; Mar2023, Vol. 151, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 161173118
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PubType: Periodical
PubTypeId: serialPeriodical
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.cor.2022.106095
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      – Code: eng
        Text: English
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      – TitleFull: Solving non-permutation flow-shop scheduling problem via a novel deep reinforcement learning approach.
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            NameFull: Wang, Zhenyu
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            NameFull: Cai, Bin
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            NameFull: Li, Jun
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            NameFull: Yang, Deheng
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            NameFull: Zhao, Yang
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            – D: 01
              M: 03
              Text: Mar2023
              Type: published
              Y: 2023
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              Value: 151
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