An evolution strategies-based reinforcement learning algorithm for multi-objective dynamic parallel machine scheduling problems
•Proposed evolution strategies-based reinforcement learning algorithm.•A multi-agent system to generate scheduling policy for parallel machines.•Action with selecting a job directly to be sequenced is used to avoid sparse reward.•Parallel machine scheduling problem with multi-objective MILP model.•M...
Uložené v:
| Vydané v: | Swarm and evolutionary computation Ročník 95; s. 101944 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
01.06.2025
|
| Predmet: | |
| ISSN: | 2210-6502 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | •Proposed evolution strategies-based reinforcement learning algorithm.•A multi-agent system to generate scheduling policy for parallel machines.•Action with selecting a job directly to be sequenced is used to avoid sparse reward.•Parallel machine scheduling problem with multi-objective MILP model.•Minimized completion time, tardiness and energy consumption simultaneously.
The multi-objective dynamic parallel machine scheduling (PMS) problem is a complex combinatorial optimization challenge encountered in manufacturing systems. Various uncertainties exist in the real-world dynamic PMS problem, such as job release time, processing time, and flexible preventive maintenance for machines. The goal is simultaneously optimizing multiple objectives under dynamic and uncertain environments, such as makespan, total tardiness, and energy consumption. This paper proposes an evolution strategies-based reinforcement learning (ESRL) algorithm to address the current multi-objective dynamic PMS problem. The proposed algorithm leverages the exploration capabilities of evolution strategies to evolve effective policies for reinforcement learning in dynamic scheduling. Moreover, the efficiency of the ESRL algorithm is enhanced by implanting three features: a) train the policy to iteratively produce the sequence directly and mitigate the sparse reward issue resulting from the symmetry inherent in the given problem; b) a multi-agent system with independent interaction and centralized training to generate the PMS policy simultaneously; c) a non-dominated sorting mechanism to determine fitness function. Extensive computational experimental results show that the ESRL algorithm outperforms the comparison state-of-the-art evolutionary algorithms and priority dispatching rules in terms of solution quality, convergence, and efficiency, with the advantage of the C-matrix exceeding 60 %, and the advantages in GD and NR surpassing 50 %. Furthermore, ablation experiments demonstrate the significant contributions of additional features in ESRL in enhancing the algorithm's performance. Meanwhile, the results of generalization experiments indicate that the ESRL quickly generates Pareto optimal solutions allowing the trained model to make optimal scheduling decisions. |
|---|---|
| AbstractList | •Proposed evolution strategies-based reinforcement learning algorithm.•A multi-agent system to generate scheduling policy for parallel machines.•Action with selecting a job directly to be sequenced is used to avoid sparse reward.•Parallel machine scheduling problem with multi-objective MILP model.•Minimized completion time, tardiness and energy consumption simultaneously.
The multi-objective dynamic parallel machine scheduling (PMS) problem is a complex combinatorial optimization challenge encountered in manufacturing systems. Various uncertainties exist in the real-world dynamic PMS problem, such as job release time, processing time, and flexible preventive maintenance for machines. The goal is simultaneously optimizing multiple objectives under dynamic and uncertain environments, such as makespan, total tardiness, and energy consumption. This paper proposes an evolution strategies-based reinforcement learning (ESRL) algorithm to address the current multi-objective dynamic PMS problem. The proposed algorithm leverages the exploration capabilities of evolution strategies to evolve effective policies for reinforcement learning in dynamic scheduling. Moreover, the efficiency of the ESRL algorithm is enhanced by implanting three features: a) train the policy to iteratively produce the sequence directly and mitigate the sparse reward issue resulting from the symmetry inherent in the given problem; b) a multi-agent system with independent interaction and centralized training to generate the PMS policy simultaneously; c) a non-dominated sorting mechanism to determine fitness function. Extensive computational experimental results show that the ESRL algorithm outperforms the comparison state-of-the-art evolutionary algorithms and priority dispatching rules in terms of solution quality, convergence, and efficiency, with the advantage of the C-matrix exceeding 60 %, and the advantages in GD and NR surpassing 50 %. Furthermore, ablation experiments demonstrate the significant contributions of additional features in ESRL in enhancing the algorithm's performance. Meanwhile, the results of generalization experiments indicate that the ESRL quickly generates Pareto optimal solutions allowing the trained model to make optimal scheduling decisions. |
| ArticleNumber | 101944 |
| Author | Zhang, Junjie Mumtaz, Jabir Huang, Shenquan Zhou, Shengwei Chen, Yarong |
| Author_xml | – sequence: 1 givenname: Yarong orcidid: 0000-0002-9057-9938 surname: Chen fullname: Chen, Yarong email: 00131011@wzu.edu.cn organization: College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, China – sequence: 2 givenname: Junjie orcidid: 0009-0008-9951-5149 surname: Zhang fullname: Zhang, Junjie email: 22451439040@stu.wzu.edu.cn organization: College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, China – sequence: 3 givenname: Jabir orcidid: 0000-0002-6386-272X surname: Mumtaz fullname: Mumtaz, Jabir email: jabirmumtaz@wzu.edu.cn, jabirmumtaz@live.com organization: College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, China – sequence: 4 givenname: Shenquan orcidid: 0000-0001-5977-5191 surname: Huang fullname: Huang, Shenquan email: hshenquan@wzu.edu.cn organization: College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, China – sequence: 5 givenname: Shengwei orcidid: 0000-0003-1388-0070 surname: Zhou fullname: Zhou, Shengwei email: 20461439014@stu.wzu.edu.cn organization: College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, China |
| BookMark | eNqFkLtOAzEQRV0EiQD5Ahr_wAbb-3RBEUW8pEg0UFte72zildeObCcoFb-Ol1BRwDQjzZ0z0pwrNLPOAkK3lCwpodXdsAwfcHRLRlg5TXhRzNCcMUqyqiTsEi1CGEiqKi2UfI4-VxYnwByidhaH6GWErYaQtTJAhz1o2zuvYAQbsQHprbZbLM3WeR13I04hHg8m6sy1A6ioj4C7k5WjVngvvTQGDB6l2mkLOKgddAczXdh71xoYww266KUJsPjp1-j98eFt_ZxtXp9e1qtNpnJSxqwikrK67ktg0HPJiczLplWqpaRQuSpy3pSS8p5QxoE2NeF9w7sacihkSpr8GuXnu8q7EDz0Yu_1KP1JUCImdWIQ3-rEpE6c1SWK_6KUjnJSlURp8w97f2YhvXXU4EVQGqyCTvskSnRO_8l_AUIYk1Q |
| CitedBy_id | crossref_primary_10_1016_j_jii_2025_100927 crossref_primary_10_1016_j_swevo_2025_102141 |
| Cites_doi | 10.1016/j.eswa.2023.120600 10.1016/j.cor.2023.106304 10.1080/00207543.2020.1775911 10.1016/j.apm.2013.01.050 10.1016/j.apenergy.2023.121332 10.1007/s10845-023-02094-4 10.1016/j.asoc.2020.106208 10.1080/00207543.2020.1812752 10.1016/j.cie.2023.109255 10.1016/j.ejor.2021.08.007 10.1177/18479790241301164 10.1016/j.ejor.2020.07.020 10.1080/00207543.2022.2058432 10.1016/j.comnet.2021.107969 10.1016/j.asoc.2023.110596 10.1016/j.cor.2023.106294 10.1016/j.cor.2013.09.016 10.1016/j.cor.2024.106933 10.1038/nature14539 10.1016/j.cor.2023.106511 10.1016/j.cie.2020.106749 10.1016/j.jclepro.2021.128867 10.1016/j.eswa.2021.114666 10.3390/sym13081521 10.1016/j.swevo.2024.101660 10.1016/j.cor.2021.105291 10.1016/j.asoc.2011.02.022 10.1016/j.eswa.2024.125616 10.1080/00207543.2021.1887533 10.1016/j.cie.2021.107489 10.23919/CSMS.2021.0027 10.1016/j.swevo.2020.100694 10.1016/j.swevo.2024.101808 10.1007/s00170-015-7657-2 10.1016/j.swevo.2018.03.011 10.1016/j.jmsy.2020.02.004 10.1109/ACCESS.2021.3097254 10.1007/s00170-011-3317-3 10.3390/sym11060729 10.1016/j.eswa.2019.04.056 10.1016/j.jmsy.2024.11.004 10.1109/TSMC.2023.3289322 10.1007/s00170-006-0662-8 10.1016/j.cor.2024.106776 10.1016/j.cor.2023.106484 10.1016/j.cor.2024.106709 10.1016/j.eswa.2023.120495 10.3389/fieng.2024.1337174 10.1016/j.eij.2023.05.008 10.1007/s13762-024-05595-8 10.1016/j.swevo.2023.101321 10.1007/s10845-021-01847-3 10.1109/4235.996017 10.1109/ACCESS.2021.3071729 10.1016/j.eswa.2022.117380 10.1002/amp2.10119 10.1016/j.cie.2018.03.039 10.1016/j.jmsy.2015.07.002 10.1016/j.cor.2011.07.019 10.1007/s10845-018-1454-3 |
| ContentType | Journal Article |
| Copyright | 2025 Elsevier B.V. |
| Copyright_xml | – notice: 2025 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.swevo.2025.101944 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| ExternalDocumentID | 10_1016_j_swevo_2025_101944 S2210650225001026 |
| GroupedDBID | --K --M .~1 0R~ 1~. 1~5 4.4 457 4G. 5VS 7-5 8P~ AAAKF AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AATLK AATTM AAXKI AAXUO AAYFN AAYWO ABAOU ABBOA ABGRD ABJNI ABMAC ABUCO ABWVN ABXDB ACDAQ ACGFS ACNNM ACRLP ACRPL ACZNC ADBBV ADEZE ADMUD ADNMO ADQTV ADTZH AEBSH AECPX AEIPS AEKER AENEX AEQOU AFJKZ AFTJW AFXIZ AGCQF AGHFR AGRNS AGUBO AGYEJ AHJVU AHZHX AIALX AIEXJ AIIUN AIKHN AITUG AKRWK ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU AOUOD APLSM APXCP ARUGR AXJTR BJAXD BKOJK BLXMC BNPGV EBS EFJIC EJD FDB FEDTE FIRID FNPLU FYGXN GBLVA GBOLZ HAMUX HVGLF HZ~ J1W JJJVA KOM M41 MHUIS MO0 N9A O-L O9- OAUVE P-8 P-9 PC. Q38 RIG ROL SDF SES SPC SPCBC SSA SSB SSD SSH SST SSV SSW SSZ T5K ~G- AAYXX ACLOT ACVFH ADCNI AEUPX AFPUW AIGII AKBMS AKYEP CITATION EFKBS EFLBG ~HD |
| ID | FETCH-LOGICAL-c305t-60a1277f5e2ef9a90a358bccb104c3c43985a19f0129e18709f89d7e3e4a85a83 |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001476858400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2210-6502 |
| IngestDate | Thu Nov 13 04:35:39 EST 2025 Tue Nov 18 22:32:41 EST 2025 Sat Jun 07 17:02:38 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Energy consumption Multi-objective optimization Evolution strategies Multi-agent, reinforcement learning Parallel machine scheduling problem |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c305t-60a1277f5e2ef9a90a358bccb104c3c43985a19f0129e18709f89d7e3e4a85a83 |
| ORCID | 0009-0008-9951-5149 0000-0003-1388-0070 0000-0002-6386-272X 0000-0001-5977-5191 0000-0002-9057-9938 |
| ParticipantIDs | crossref_primary_10_1016_j_swevo_2025_101944 crossref_citationtrail_10_1016_j_swevo_2025_101944 elsevier_sciencedirect_doi_10_1016_j_swevo_2025_101944 |
| PublicationCentury | 2000 |
| PublicationDate | June 2025 2025-06-00 |
| PublicationDateYYYYMMDD | 2025-06-01 |
| PublicationDate_xml | – month: 06 year: 2025 text: June 2025 |
| PublicationDecade | 2020 |
| PublicationTitle | Swarm and evolutionary computation |
| PublicationYear | 2025 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Zhang (bib0063) 2012; 39 Ke (bib0041) 2023; 45 Xu (bib0028) 2021; 13 Salimans, T., et al. Pfund, Fowler, Gupta (bib0003) 2004; 21 Wang, Feng, Wang (bib0069) 2023; 80 Chen (bib0047) 2023 Lei, Yuan, Cai (bib0030) 2021; 59 Li (bib0006) 2024 Lang (bib0055) 2021; 172 Shiue, Lee, Su (bib0020) 2018; 125 Hu (bib0059) 2020; 55 Caselli, G., et al. Parichehreh (bib0053) 2024; 21 arXiv preprint arXiv:2308.13420, 2023. Li, Gong, Lu (bib0074) 2022; 203 Chen (bib0027) 2023; 229 Chyu, Chang (bib0029) 2011; 57 Anghinolfi, Paolucci, Ronco (bib0034) 2021; 289 Chen (bib0032) 2022; 13 Silva (bib0019) 2019; 131 Luo (bib0061) 2020; 91 Deb (bib0073) 2002; 6 Caselli (bib0014) 2024; 163 Liu (bib0023) 2021; 59 Luo, Zhang, Fan (bib0066) 2021; 159 LeCun, Bengio, Hinton (bib0071) 2015; 521 Bandyopadhyay, Bhattacharya (bib0018) 2013; 37 Song, Y., et al. Song (bib0033) 2021; 9 Gui (bib0022) 2023; 180 Wang, Pan, Wang (bib0070) 2021; 1 Shakya, Pillai, Chakrabarty (bib0043) 2023; 231 Wang (bib0065) 2021; 190 Li (bib0036) 2016; 84 Aghaei, Amjady, Shayanfar (bib0076) 2011; 11 Brammer, Lutz, Neumann (bib0060) 2022; 299 Fathollahi-Fard (bib0075) 2023; 158 Zhang (bib0008) 2024; 2 Zhang (bib0050) 2025; 92 Yue (bib0044) 2024; 90 Liu, Piplani, Toro (bib0062) 2022; 60 Li (bib0052) 2024; 35 Kayhan, Yildiz (bib0004) 2023; 34 Bitar, Dauzère-Pérès, Yugma (bib0017) 2021; 132 Wang (bib0045) 2024; 77 Drugan (bib0010) 2019; 44 Liu, Piplani, Toro (bib0068) 2023 arXiv preprint arXiv:1703.03864, 2017. Wang (bib0021) 2020; 31 Wang, Li, Gong (bib0031) 2023; 24 Ying, Pourhejazy, Huang (bib0001) 2024 Liu (bib0026) 2023 Bahroun (bib0002) 2024; 16 Zhang, Zheng, Weng (bib0048) 2007; 34 Lee, Kim (bib0067) 2022; 60 Xiao (bib0037) 2021; 320 Kazemi (bib0040) 2021; 38 Su (bib0056) 2023; 145 Li (bib0009) 2020; 56 Chen (bib0005) 2024 Tao (bib0024) 2014; 43 Chen (bib0038) 2025; 176 Chen (bib0013) 2023; 14 Lu, Shi, Pei (bib0015) 2024; 170 Chen (bib0025) 2021; 38 Yuan, Jiang, Wang (bib0049) 2016; 8 Majid (bib0058) 2023 Khalid (bib0042) 2019; 11 Parichehreh (bib0016) 2024 Springer. Julaiti (bib0051) 2022; 4 Muñoz-Díaz, Escudero-Santana, Lorenzo-Espejo (bib0012) 2024; 163 Chen (bib0007) 2025; 262 Wang, Liu (bib0035) 2015; 37 Zhang, Clune, Stanley (bib0057) 2017 Paeng, Park, Park (bib0072) 2021; 9 Hu (bib0064) 2020; 149 Che (bib0046) 2023; 345 Lu (10.1016/j.swevo.2025.101944_bib0015) 2024; 170 Wang (10.1016/j.swevo.2025.101944_bib0045) 2024; 77 Chen (10.1016/j.swevo.2025.101944_bib0032) 2022; 13 Ke (10.1016/j.swevo.2025.101944_bib0041) 2023; 45 10.1016/j.swevo.2025.101944_bib0054 Fathollahi-Fard (10.1016/j.swevo.2025.101944_bib0075) 2023; 158 10.1016/j.swevo.2025.101944_bib0011 Liu (10.1016/j.swevo.2025.101944_bib0068) 2023 Bitar (10.1016/j.swevo.2025.101944_bib0017) 2021; 132 Bandyopadhyay (10.1016/j.swevo.2025.101944_bib0018) 2013; 37 Wang (10.1016/j.swevo.2025.101944_bib0069) 2023; 80 Bahroun (10.1016/j.swevo.2025.101944_bib0002) 2024; 16 Shakya (10.1016/j.swevo.2025.101944_bib0043) 2023; 231 Wang (10.1016/j.swevo.2025.101944_bib0021) 2020; 31 Wang (10.1016/j.swevo.2025.101944_bib0065) 2021; 190 Chen (10.1016/j.swevo.2025.101944_bib0013) 2023; 14 Silva (10.1016/j.swevo.2025.101944_bib0019) 2019; 131 Chen (10.1016/j.swevo.2025.101944_bib0027) 2023; 229 Che (10.1016/j.swevo.2025.101944_bib0046) 2023; 345 Kayhan (10.1016/j.swevo.2025.101944_bib0004) 2023; 34 Li (10.1016/j.swevo.2025.101944_bib0006) 2024 Liu (10.1016/j.swevo.2025.101944_bib0023) 2021; 59 Lei (10.1016/j.swevo.2025.101944_bib0030) 2021; 59 Zhang (10.1016/j.swevo.2025.101944_bib0057) 2017 Li (10.1016/j.swevo.2025.101944_bib0009) 2020; 56 Xu (10.1016/j.swevo.2025.101944_bib0028) 2021; 13 Paeng (10.1016/j.swevo.2025.101944_bib0072) 2021; 9 Hu (10.1016/j.swevo.2025.101944_bib0064) 2020; 149 Ying (10.1016/j.swevo.2025.101944_bib0001) 2024 Yue (10.1016/j.swevo.2025.101944_bib0044) 2024; 90 Song (10.1016/j.swevo.2025.101944_bib0033) 2021; 9 Parichehreh (10.1016/j.swevo.2025.101944_bib0016) 2024 Li (10.1016/j.swevo.2025.101944_bib0074) 2022; 203 Yuan (10.1016/j.swevo.2025.101944_bib0049) 2016; 8 Brammer (10.1016/j.swevo.2025.101944_bib0060) 2022; 299 LeCun (10.1016/j.swevo.2025.101944_bib0071) 2015; 521 Zhang (10.1016/j.swevo.2025.101944_bib0008) 2024; 2 Hu (10.1016/j.swevo.2025.101944_bib0059) 2020; 55 Chen (10.1016/j.swevo.2025.101944_bib0007) 2025; 262 Chen (10.1016/j.swevo.2025.101944_bib0005) 2024 Gui (10.1016/j.swevo.2025.101944_bib0022) 2023; 180 Chyu (10.1016/j.swevo.2025.101944_bib0029) 2011; 57 Luo (10.1016/j.swevo.2025.101944_bib0061) 2020; 91 Tao (10.1016/j.swevo.2025.101944_bib0024) 2014; 43 Chen (10.1016/j.swevo.2025.101944_bib0038) 2025; 176 Kazemi (10.1016/j.swevo.2025.101944_bib0040) 2021; 38 Khalid (10.1016/j.swevo.2025.101944_bib0042) 2019; 11 Wang (10.1016/j.swevo.2025.101944_bib0070) 2021; 1 Shiue (10.1016/j.swevo.2025.101944_bib0020) 2018; 125 Drugan (10.1016/j.swevo.2025.101944_bib0010) 2019; 44 Zhang (10.1016/j.swevo.2025.101944_bib0063) 2012; 39 Chen (10.1016/j.swevo.2025.101944_bib0025) 2021; 38 Li (10.1016/j.swevo.2025.101944_bib0036) 2016; 84 Chen (10.1016/j.swevo.2025.101944_bib0047) 2023 Liu (10.1016/j.swevo.2025.101944_bib0026) 2023 Deb (10.1016/j.swevo.2025.101944_bib0073) 2002; 6 Wang (10.1016/j.swevo.2025.101944_bib0035) 2015; 37 Xiao (10.1016/j.swevo.2025.101944_bib0037) 2021; 320 Zhang (10.1016/j.swevo.2025.101944_bib0050) 2025; 92 Li (10.1016/j.swevo.2025.101944_bib0052) 2024; 35 Muñoz-Díaz (10.1016/j.swevo.2025.101944_bib0012) 2024; 163 Zhang (10.1016/j.swevo.2025.101944_bib0048) 2007; 34 Luo (10.1016/j.swevo.2025.101944_bib0066) 2021; 159 Su (10.1016/j.swevo.2025.101944_bib0056) 2023; 145 Pfund (10.1016/j.swevo.2025.101944_bib0003) 2004; 21 10.1016/j.swevo.2025.101944_bib0039 Julaiti (10.1016/j.swevo.2025.101944_bib0051) 2022; 4 Liu (10.1016/j.swevo.2025.101944_bib0062) 2022; 60 Wang (10.1016/j.swevo.2025.101944_bib0031) 2023; 24 Parichehreh (10.1016/j.swevo.2025.101944_bib0053) 2024; 21 Caselli (10.1016/j.swevo.2025.101944_bib0014) 2024; 163 Aghaei (10.1016/j.swevo.2025.101944_bib0076) 2011; 11 Anghinolfi (10.1016/j.swevo.2025.101944_bib0034) 2021; 289 Lee (10.1016/j.swevo.2025.101944_bib0067) 2022; 60 Lang (10.1016/j.swevo.2025.101944_bib0055) 2021; 172 Majid (10.1016/j.swevo.2025.101944_bib0058) 2023 |
| References_xml | – volume: 2 year: 2024 ident: bib0008 article-title: Enhancing multi-objective evolutionary algorithms with machine learning for scheduling problems: recent advances and survey publication-title: Front. Ind. Eng. – volume: 37 start-page: 182 year: 2015 end-page: 192 ident: bib0035 article-title: Multi-objective optimization of parallel machine scheduling integrated with multi-resources preventive maintenance planning publication-title: J. Manuf. Syst. – volume: 60 start-page: 2346 year: 2022 end-page: 2368 ident: bib0067 article-title: Reinforcement learning for robotic flow shop scheduling with processing time variations publication-title: Int. J. Prod. Res. – volume: 149 year: 2020 ident: bib0064 article-title: Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0 publication-title: Comput. Ind. Eng. – volume: 8 start-page: 94 year: 2016 end-page: 103 ident: bib0049 article-title: Dynamic parallel machine scheduling with random breakdowns using the learning agent publication-title: Int. J. Serv. Oper. Informatics – volume: 163 year: 2024 ident: bib0012 article-title: Solving an unrelated parallel machines scheduling problem with machine-and job-dependent setups and precedence constraints considering support machines publication-title: Comput. Oper. Res. – volume: 31 start-page: 417 year: 2020 end-page: 432 ident: bib0021 article-title: Adaptive job shop scheduling strategy based on weighted Q-learning algorithm publication-title: J. Intell. Manuf. – volume: 9 start-page: 56822 year: 2021 end-page: 56835 ident: bib0033 article-title: A hybrid multi-objective teaching-learning based optimization for scheduling problem of hybrid flow shop with unrelated parallel machine publication-title: IEEe Access. – volume: 84 start-page: 213 year: 2016 end-page: 226 ident: bib0036 article-title: Unrelated parallel machine scheduling problem with energy and tardiness cost publication-title: Int. J. Adv. Manuf. Technol. – volume: 43 start-page: 215 year: 2014 end-page: 224 ident: bib0024 article-title: A better online algorithm for the parallel machine scheduling to minimize the total weighted completion time publication-title: Comput. Oper. Res. – volume: 34 start-page: 968 year: 2007 end-page: 980 ident: bib0048 article-title: Dynamic parallel machine scheduling with mean weighted tardiness objective by Q-Learning publication-title: Int. J. Adv. Manuf. Technol. – year: 2024 ident: bib0001 article-title: Revisiting the development trajectory of parallel machine scheduling publication-title: Comput. Oper. Res. – volume: 38 start-page: 271 year: 2021 end-page: 284 ident: bib0025 article-title: Makespan minimization for scheduling on two identical parallel machiens with flexible maintenance and nonresumable jobs publication-title: J. Ind. Prod. Eng. – volume: 9 start-page: 101390 year: 2021 end-page: 101401 ident: bib0072 article-title: Deep reinforcement learning for minimizing tardiness in parallel machine scheduling with sequence dependent family setups publication-title: IEEe Access. – volume: 289 start-page: 416 year: 2021 end-page: 434 ident: bib0034 article-title: A bi-objective heuristic approach for green identical parallel machine scheduling publication-title: Eur. J. Oper. Res. – volume: 180 year: 2023 ident: bib0022 article-title: Dynamic scheduling for flexible job shop using a deep reinforcement learning approach publication-title: Comput. Ind. Eng. – volume: 45 start-page: 19 year: 2023 ident: bib0041 article-title: Unrelated parallel batch machine scheduling using a modified ABC algorithm publication-title: Eng. Proc. – reference: arXiv preprint arXiv:2308.13420, 2023. – volume: 21 start-page: 9651 year: 2024 end-page: 9676 ident: bib0053 article-title: An energy-efficient unrelated parallel machine scheduling problem with learning effect of operators and deterioration of jobs publication-title: Int. J. Environ. Sci. Technol. – year: 2023 ident: bib0026 article-title: Dynamic parallel machine scheduling with deep Q-network publication-title: IEEE Trans. Syst. Man Cybern. – year: 2024 ident: bib0005 article-title: Solving batch processing machine scheduling problems using a self-adaptive approach based on dynamic programming publication-title: Comput. Oper. Res. – reference: Caselli, G., et al. – volume: 35 start-page: 1107 year: 2024 end-page: 1140 ident: bib0052 article-title: A two-stage RNN-based deep reinforcement learning approach for solving the parallel machine scheduling problem with due dates and family setups publication-title: J. Intell. Manuf. – volume: 125 start-page: 604 year: 2018 end-page: 614 ident: bib0020 article-title: Real-time scheduling for a smart factory using a reinforcement learning approach publication-title: Comput. Ind. Eng. – reference: Salimans, T., et al., – volume: 16 year: 2024 ident: bib0002 article-title: Integrated proactive-reactive tool for dynamic scheduling of parallel machine operations publication-title: Int. J. Eng. Bus. Manag. – volume: 203 year: 2022 ident: bib0074 article-title: A reinforcement learning based RMOEA/D for bi-objective fuzzy flexible job shop scheduling publication-title: Expert. Syst. Appl. – volume: 14 start-page: 539 year: 2023 end-page: 554 ident: bib0013 article-title: Joint optimization of production and maintenance scheduling for unrelated parallel machine using hybrid discrete spider monkey optimization algorithm publication-title: Int. J. Ind. Eng. Comput. – volume: 55 start-page: 1 year: 2020 end-page: 14 ident: bib0059 article-title: Petri-net-based dynamic scheduling of flexible manufacturing system via deep reinforcement learning with graph convolutional network publication-title: J. Manuf. Syst. – reference: . Springer. – volume: 11 start-page: 729 year: 2019 ident: bib0042 article-title: Hybrid particle swarm algorithm for products’ scheduling problem in cellular manufacturing system publication-title: Symmetry (Basel) – start-page: 1 year: 2024 end-page: 26 ident: bib0016 article-title: An energy-efficient unrelated parallel machine scheduling problem with learning effect of operators and deterioration of jobs publication-title: Int. J. Environ. Sci. Technol. – volume: 262 year: 2025 ident: bib0007 article-title: An efficient Q-learning integrated multi-objective hyper-heuristic approach for hybrid flow shop scheduling problems with lot streaming publication-title: Expert. Syst. Appl. – volume: 38 start-page: 157 year: 2021 end-page: 170 ident: bib0040 article-title: The integrated production-distribution scheduling in parallel machine environment by using improved genetic algorithms publication-title: J. Ind. Prod. Eng. – volume: 34 start-page: 905 year: 2023 end-page: 929 ident: bib0004 article-title: Reinforcement learning applications to machine scheduling problems: a comprehensive literature review publication-title: J. Intell. Manuf. – volume: 24 year: 2023 ident: bib0031 article-title: Minimizing tardiness and makespan for distributed heterogeneous unrelated parallel machine scheduling by knowledge and Pareto-based memetic algorithm publication-title: Egyptian Informatics J. – volume: 163 year: 2024 ident: bib0014 article-title: Exact algorithms for a parallel machine scheduling problem with workforce and contiguity constraints publication-title: Comput. Oper. Res. – volume: 176 year: 2025 ident: bib0038 article-title: Batch processing machine scheduling problems using a self-adaptive approach based on dynamic programming publication-title: Comput. Oper. Res. – volume: 158 year: 2023 ident: bib0075 article-title: Efficient multi-objective metaheuristic algorithm for sustainable harvest planning problem publication-title: Comput. Oper. Res. – volume: 132 year: 2021 ident: bib0017 article-title: Unrelated parallel machine scheduling with new criteria: complexity and models publication-title: Comput. Oper. Res. – volume: 320 year: 2021 ident: bib0037 article-title: A branch and bound algorithm for a parallel machine scheduling problem in green manufacturing industry considering time cost and power consumption publication-title: J. Clean. Prod. – volume: 170 year: 2024 ident: bib0015 article-title: A distributionally robust approach for the parallel machine scheduling problem with optional machines and job tardiness publication-title: Comput. Oper. Res. – volume: 299 start-page: 75 year: 2022 end-page: 86 ident: bib0060 article-title: Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning publication-title: Eur. J. Oper. Res. – volume: 77 start-page: 946 year: 2024 end-page: 961 ident: bib0045 article-title: Generative deep reinforcement learning method for dynamic parallel machines scheduling with adaptive maintenance activities publication-title: J. Manuf. Syst. – volume: 39 start-page: 1315 year: 2012 end-page: 1324 ident: bib0063 article-title: Minimizing mean weighted tardiness in unrelated parallel machine scheduling with reinforcement learning publication-title: Comput. Oper. Res. – volume: 11 start-page: 3846 year: 2011 end-page: 3858 ident: bib0076 article-title: Multi-objective electricity market clearing considering dynamic security by lexicographic optimization and augmented epsilon constraint method publication-title: Appl. Soft. Comput. – year: 2023 ident: bib0068 article-title: A deep multi-agent reinforcement learning approach to solve dynamic job shop scheduling problem publication-title: Comput. Oper. Res. – volume: 1 start-page: 257 year: 2021 end-page: 270 ident: bib0070 article-title: A review of reinforcement learning based intelligent optimization for manufacturing scheduling publication-title: Complex Syst. Model. Simul. – volume: 56 year: 2020 ident: bib0009 article-title: Evolution strategies for continuous optimization: a survey of the state-of-the-art publication-title: Swarm. Evol. Comput. – year: 2023 ident: bib0047 article-title: An improved spider monkey optimization algorithm for multi-objective planning and scheduling problems of PCB assembly line publication-title: Expert. Syst. Appl. – volume: 145 year: 2023 ident: bib0056 article-title: Evolution strategies-based optimized graph reinforcement learning for solving dynamic job shop scheduling problem publication-title: Appl. Soft. Comput. – volume: 37 start-page: 6718 year: 2013 end-page: 6729 ident: bib0018 article-title: Solving multi-objective parallel machine scheduling problem by a modified NSGA-II publication-title: Appl. Math. Model. – year: 2023 ident: bib0058 article-title: versus publication-title: IEEe Trans. Neural Netw. Learn. Syst. – volume: 80 year: 2023 ident: bib0069 article-title: Bi-objective scenario-guided swarm intelligent algorithms based on reinforcement learning for robust unrelated parallel machines scheduling with setup times publication-title: Swarm. Evol. Comput. – year: 2017 ident: bib0057 article-title: On the relationship between the OpenAI evolution strategy and stochastic gradient descent publication-title: arXiv preprint – volume: 229 year: 2023 ident: bib0027 article-title: An improved spider monkey optimization algorithm for multi-objective planning and scheduling problems of PCB assembly line publication-title: Expert. Syst. Appl. – volume: 13 start-page: 1521 year: 2021 ident: bib0028 article-title: Modeling and optimization for multi-objective nonidentical parallel machining line scheduling with a jumping process operation constraint publication-title: Symmetry. (Basel) – volume: 92 year: 2025 ident: bib0050 article-title: A revised deep reinforcement learning algorithm for parallel machine scheduling problem under multi-scenario due date constraints publication-title: Swarm. Evol. Comput. – volume: 231 year: 2023 ident: bib0043 article-title: Reinforcement learning algorithms: a brief survey publication-title: Expert. Syst. Appl. – reference: Song, Y., et al., – volume: 91 year: 2020 ident: bib0061 article-title: Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning publication-title: Appl. Soft. Comput. – start-page: 1 year: 2024 end-page: 34 ident: bib0006 article-title: A transformer-based deep reinforcement learning approach for dynamic parallel machine scheduling problem with family setups publication-title: J. Intell. Manuf. – volume: 59 start-page: 5259 year: 2021 end-page: 5271 ident: bib0030 article-title: An improved artificial bee colony for multi-objective distributed unrelated parallel machine scheduling publication-title: Int. J. Prod. Res. – reference: arXiv preprint arXiv:1703.03864, 2017. – volume: 131 start-page: 148 year: 2019 end-page: 171 ident: bib0019 article-title: A reinforcement learning-based multi-agent framework applied for solving routing and scheduling problems publication-title: Expert. Syst. Appl. – volume: 60 start-page: 4049 year: 2022 end-page: 4069 ident: bib0062 article-title: Deep reinforcement learning for dynamic scheduling of a flexible job shop publication-title: Int. J. Prod. Res. – volume: 44 start-page: 228 year: 2019 end-page: 246 ident: bib0010 article-title: versus publication-title: Swarm. Evol. Comput. – volume: 4 year: 2022 ident: bib0051 article-title: Stochastic parallel machine scheduling using reinforcement learning publication-title: J. Adv. Manuf. Process. – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: bib0071 article-title: Deep learning publication-title: Nature – volume: 57 start-page: 763 year: 2011 end-page: 776 ident: bib0029 article-title: Optimizing fuzzy makespan and tardiness for unrelated parallel machine scheduling with archived metaheuristics publication-title: Int. J. Adv. Manuf. Technol. – volume: 6 start-page: 182 year: 2002 end-page: 197 ident: bib0073 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. – volume: 172 year: 2021 ident: bib0055 article-title: NeuroEvolution of augmenting topologies for solving a two-stage hybrid flow shop scheduling problem: a comparison of different solution strategies publication-title: Expert. Syst. Appl. – volume: 159 year: 2021 ident: bib0066 article-title: Dynamic multi-objective scheduling for flexible job shop by deep reinforcement learning publication-title: Comput. Ind. Eng. – volume: 190 year: 2021 ident: bib0065 article-title: Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning publication-title: Comput. Netw. – volume: 59 start-page: 6327 year: 2021 end-page: 6346 ident: bib0023 article-title: Parallel machine scheduling with stochastic release times and processing times publication-title: Int. J. Prod. Res. – volume: 345 year: 2023 ident: bib0046 article-title: A deep reinforcement learning based multi-objective optimization for the scheduling of oxygen production system in integrated iron and steel plants publication-title: Appl. Energy – volume: 21 start-page: 230 year: 2004 end-page: 241 ident: bib0003 article-title: A survey of algorithms for single and multi-objective unrelated parallel-machine deterministic scheduling problems publication-title: J. Chin. Inst. Ind. Engineers – volume: 90 year: 2024 ident: bib0044 article-title: Two-stage double deep Q-network algorithm considering external non-dominant set for multi-objective dynamic flexible job shop scheduling problems publication-title: Swarm. Evol. Comput. – volume: 13 start-page: 457 year: 2022 end-page: 472 ident: bib0032 article-title: Bi-objective optimization of identical parallel machine scheduling with flexible maintenance and job release times publication-title: Int. J. Ind. Eng. Comput. – year: 2023 ident: 10.1016/j.swevo.2025.101944_bib0047 article-title: An improved spider monkey optimization algorithm for multi-objective planning and scheduling problems of PCB assembly line publication-title: Expert. Syst. Appl. doi: 10.1016/j.eswa.2023.120600 – volume: 158 year: 2023 ident: 10.1016/j.swevo.2025.101944_bib0075 article-title: Efficient multi-objective metaheuristic algorithm for sustainable harvest planning problem publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2023.106304 – volume: 59 start-page: 5259 issue: 17 year: 2021 ident: 10.1016/j.swevo.2025.101944_bib0030 article-title: An improved artificial bee colony for multi-objective distributed unrelated parallel machine scheduling publication-title: Int. J. Prod. Res. doi: 10.1080/00207543.2020.1775911 – volume: 37 start-page: 6718 issue: 10–11 year: 2013 ident: 10.1016/j.swevo.2025.101944_bib0018 article-title: Solving multi-objective parallel machine scheduling problem by a modified NSGA-II publication-title: Appl. Math. Model. doi: 10.1016/j.apm.2013.01.050 – volume: 345 year: 2023 ident: 10.1016/j.swevo.2025.101944_bib0046 article-title: A deep reinforcement learning based multi-objective optimization for the scheduling of oxygen production system in integrated iron and steel plants publication-title: Appl. Energy doi: 10.1016/j.apenergy.2023.121332 – volume: 35 start-page: 1107 issue: 3 year: 2024 ident: 10.1016/j.swevo.2025.101944_bib0052 article-title: A two-stage RNN-based deep reinforcement learning approach for solving the parallel machine scheduling problem with due dates and family setups publication-title: J. Intell. Manuf. doi: 10.1007/s10845-023-02094-4 – volume: 91 year: 2020 ident: 10.1016/j.swevo.2025.101944_bib0061 article-title: Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2020.106208 – volume: 59 start-page: 6327 issue: 20 year: 2021 ident: 10.1016/j.swevo.2025.101944_bib0023 article-title: Parallel machine scheduling with stochastic release times and processing times publication-title: Int. J. Prod. Res. doi: 10.1080/00207543.2020.1812752 – volume: 38 start-page: 157 issue: 3 year: 2021 ident: 10.1016/j.swevo.2025.101944_bib0040 article-title: The integrated production-distribution scheduling in parallel machine environment by using improved genetic algorithms publication-title: J. Ind. Prod. Eng. – volume: 180 year: 2023 ident: 10.1016/j.swevo.2025.101944_bib0022 article-title: Dynamic scheduling for flexible job shop using a deep reinforcement learning approach publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2023.109255 – volume: 299 start-page: 75 issue: 1 year: 2022 ident: 10.1016/j.swevo.2025.101944_bib0060 article-title: Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2021.08.007 – volume: 16 year: 2024 ident: 10.1016/j.swevo.2025.101944_bib0002 article-title: Integrated proactive-reactive tool for dynamic scheduling of parallel machine operations publication-title: Int. J. Eng. Bus. Manag. doi: 10.1177/18479790241301164 – start-page: 1 year: 2024 ident: 10.1016/j.swevo.2025.101944_bib0016 article-title: An energy-efficient unrelated parallel machine scheduling problem with learning effect of operators and deterioration of jobs publication-title: Int. J. Environ. Sci. Technol. – year: 2024 ident: 10.1016/j.swevo.2025.101944_bib0005 article-title: Solving batch processing machine scheduling problems using a self-adaptive approach based on dynamic programming publication-title: Comput. Oper. Res. – volume: 38 start-page: 271 issue: 4 year: 2021 ident: 10.1016/j.swevo.2025.101944_bib0025 article-title: Makespan minimization for scheduling on two identical parallel machiens with flexible maintenance and nonresumable jobs publication-title: J. Ind. Prod. Eng. – volume: 289 start-page: 416 issue: 2 year: 2021 ident: 10.1016/j.swevo.2025.101944_bib0034 article-title: A bi-objective heuristic approach for green identical parallel machine scheduling publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2020.07.020 – volume: 60 start-page: 4049 issue: 13 year: 2022 ident: 10.1016/j.swevo.2025.101944_bib0062 article-title: Deep reinforcement learning for dynamic scheduling of a flexible job shop publication-title: Int. J. Prod. Res. doi: 10.1080/00207543.2022.2058432 – volume: 8 start-page: 94 issue: 2 year: 2016 ident: 10.1016/j.swevo.2025.101944_bib0049 article-title: Dynamic parallel machine scheduling with random breakdowns using the learning agent publication-title: Int. J. Serv. Oper. Informatics – volume: 190 year: 2021 ident: 10.1016/j.swevo.2025.101944_bib0065 article-title: Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning publication-title: Comput. Netw. doi: 10.1016/j.comnet.2021.107969 – volume: 229 year: 2023 ident: 10.1016/j.swevo.2025.101944_bib0027 article-title: An improved spider monkey optimization algorithm for multi-objective planning and scheduling problems of PCB assembly line publication-title: Expert. Syst. Appl. doi: 10.1016/j.eswa.2023.120600 – volume: 145 year: 2023 ident: 10.1016/j.swevo.2025.101944_bib0056 article-title: Evolution strategies-based optimized graph reinforcement learning for solving dynamic job shop scheduling problem publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2023.110596 – year: 2023 ident: 10.1016/j.swevo.2025.101944_bib0068 article-title: A deep multi-agent reinforcement learning approach to solve dynamic job shop scheduling problem publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2023.106294 – volume: 43 start-page: 215 year: 2014 ident: 10.1016/j.swevo.2025.101944_bib0024 article-title: A better online algorithm for the parallel machine scheduling to minimize the total weighted completion time publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2013.09.016 – ident: 10.1016/j.swevo.2025.101944_bib0054 – ident: 10.1016/j.swevo.2025.101944_bib0039 – volume: 176 year: 2025 ident: 10.1016/j.swevo.2025.101944_bib0038 article-title: Batch processing machine scheduling problems using a self-adaptive approach based on dynamic programming publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2024.106933 – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 10.1016/j.swevo.2025.101944_bib0071 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 163 year: 2024 ident: 10.1016/j.swevo.2025.101944_bib0012 article-title: Solving an unrelated parallel machines scheduling problem with machine-and job-dependent setups and precedence constraints considering support machines publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2023.106511 – volume: 149 year: 2020 ident: 10.1016/j.swevo.2025.101944_bib0064 article-title: Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0 publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2020.106749 – volume: 320 year: 2021 ident: 10.1016/j.swevo.2025.101944_bib0037 article-title: A branch and bound algorithm for a parallel machine scheduling problem in green manufacturing industry considering time cost and power consumption publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2021.128867 – volume: 172 year: 2021 ident: 10.1016/j.swevo.2025.101944_bib0055 article-title: NeuroEvolution of augmenting topologies for solving a two-stage hybrid flow shop scheduling problem: a comparison of different solution strategies publication-title: Expert. Syst. Appl. doi: 10.1016/j.eswa.2021.114666 – volume: 13 start-page: 1521 issue: 8 year: 2021 ident: 10.1016/j.swevo.2025.101944_bib0028 article-title: Modeling and optimization for multi-objective nonidentical parallel machining line scheduling with a jumping process operation constraint publication-title: Symmetry. (Basel) doi: 10.3390/sym13081521 – volume: 90 year: 2024 ident: 10.1016/j.swevo.2025.101944_bib0044 article-title: Two-stage double deep Q-network algorithm considering external non-dominant set for multi-objective dynamic flexible job shop scheduling problems publication-title: Swarm. Evol. Comput. doi: 10.1016/j.swevo.2024.101660 – volume: 132 year: 2021 ident: 10.1016/j.swevo.2025.101944_bib0017 article-title: Unrelated parallel machine scheduling with new criteria: complexity and models publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2021.105291 – volume: 45 start-page: 19 issue: 1 year: 2023 ident: 10.1016/j.swevo.2025.101944_bib0041 article-title: Unrelated parallel batch machine scheduling using a modified ABC algorithm publication-title: Eng. Proc. – year: 2017 ident: 10.1016/j.swevo.2025.101944_bib0057 article-title: On the relationship between the OpenAI evolution strategy and stochastic gradient descent publication-title: arXiv preprint – volume: 11 start-page: 3846 issue: 4 year: 2011 ident: 10.1016/j.swevo.2025.101944_bib0076 article-title: Multi-objective electricity market clearing considering dynamic security by lexicographic optimization and augmented epsilon constraint method publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2011.02.022 – volume: 262 year: 2025 ident: 10.1016/j.swevo.2025.101944_bib0007 article-title: An efficient Q-learning integrated multi-objective hyper-heuristic approach for hybrid flow shop scheduling problems with lot streaming publication-title: Expert. Syst. Appl. doi: 10.1016/j.eswa.2024.125616 – volume: 60 start-page: 2346 issue: 7 year: 2022 ident: 10.1016/j.swevo.2025.101944_bib0067 article-title: Reinforcement learning for robotic flow shop scheduling with processing time variations publication-title: Int. J. Prod. Res. doi: 10.1080/00207543.2021.1887533 – volume: 159 year: 2021 ident: 10.1016/j.swevo.2025.101944_bib0066 article-title: Dynamic multi-objective scheduling for flexible job shop by deep reinforcement learning publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2021.107489 – volume: 1 start-page: 257 issue: 4 year: 2021 ident: 10.1016/j.swevo.2025.101944_bib0070 article-title: A review of reinforcement learning based intelligent optimization for manufacturing scheduling publication-title: Complex Syst. Model. Simul. doi: 10.23919/CSMS.2021.0027 – volume: 56 year: 2020 ident: 10.1016/j.swevo.2025.101944_bib0009 article-title: Evolution strategies for continuous optimization: a survey of the state-of-the-art publication-title: Swarm. Evol. Comput. doi: 10.1016/j.swevo.2020.100694 – volume: 92 year: 2025 ident: 10.1016/j.swevo.2025.101944_bib0050 article-title: A revised deep reinforcement learning algorithm for parallel machine scheduling problem under multi-scenario due date constraints publication-title: Swarm. Evol. Comput. doi: 10.1016/j.swevo.2024.101808 – volume: 84 start-page: 213 year: 2016 ident: 10.1016/j.swevo.2025.101944_bib0036 article-title: Unrelated parallel machine scheduling problem with energy and tardiness cost publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-015-7657-2 – volume: 44 start-page: 228 year: 2019 ident: 10.1016/j.swevo.2025.101944_bib0010 article-title: Reinforcement learning versus evolutionary computation: a survey on hybrid algorithms publication-title: Swarm. Evol. Comput. doi: 10.1016/j.swevo.2018.03.011 – volume: 55 start-page: 1 year: 2020 ident: 10.1016/j.swevo.2025.101944_bib0059 article-title: Petri-net-based dynamic scheduling of flexible manufacturing system via deep reinforcement learning with graph convolutional network publication-title: J. Manuf. Syst. doi: 10.1016/j.jmsy.2020.02.004 – start-page: 1 year: 2024 ident: 10.1016/j.swevo.2025.101944_bib0006 article-title: A transformer-based deep reinforcement learning approach for dynamic parallel machine scheduling problem with family setups publication-title: J. Intell. Manuf. – volume: 9 start-page: 101390 year: 2021 ident: 10.1016/j.swevo.2025.101944_bib0072 article-title: Deep reinforcement learning for minimizing tardiness in parallel machine scheduling with sequence dependent family setups publication-title: IEEe Access. doi: 10.1109/ACCESS.2021.3097254 – volume: 57 start-page: 763 year: 2011 ident: 10.1016/j.swevo.2025.101944_bib0029 article-title: Optimizing fuzzy makespan and tardiness for unrelated parallel machine scheduling with archived metaheuristics publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-011-3317-3 – volume: 11 start-page: 729 issue: 6 year: 2019 ident: 10.1016/j.swevo.2025.101944_bib0042 article-title: Hybrid particle swarm algorithm for products’ scheduling problem in cellular manufacturing system publication-title: Symmetry (Basel) doi: 10.3390/sym11060729 – volume: 131 start-page: 148 year: 2019 ident: 10.1016/j.swevo.2025.101944_bib0019 article-title: A reinforcement learning-based multi-agent framework applied for solving routing and scheduling problems publication-title: Expert. Syst. Appl. doi: 10.1016/j.eswa.2019.04.056 – volume: 77 start-page: 946 year: 2024 ident: 10.1016/j.swevo.2025.101944_bib0045 article-title: Generative deep reinforcement learning method for dynamic parallel machines scheduling with adaptive maintenance activities publication-title: J. Manuf. Syst. doi: 10.1016/j.jmsy.2024.11.004 – year: 2023 ident: 10.1016/j.swevo.2025.101944_bib0026 article-title: Dynamic parallel machine scheduling with deep Q-network publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/TSMC.2023.3289322 – volume: 34 start-page: 968 year: 2007 ident: 10.1016/j.swevo.2025.101944_bib0048 article-title: Dynamic parallel machine scheduling with mean weighted tardiness objective by Q-Learning publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-006-0662-8 – volume: 170 year: 2024 ident: 10.1016/j.swevo.2025.101944_bib0015 article-title: A distributionally robust approach for the parallel machine scheduling problem with optional machines and job tardiness publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2024.106776 – volume: 163 year: 2024 ident: 10.1016/j.swevo.2025.101944_bib0014 article-title: Exact algorithms for a parallel machine scheduling problem with workforce and contiguity constraints publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2023.106484 – year: 2023 ident: 10.1016/j.swevo.2025.101944_bib0058 article-title: Deep reinforcement learning versus evolution strategies: a comparative survey publication-title: IEEe Trans. Neural Netw. Learn. Syst. – year: 2024 ident: 10.1016/j.swevo.2025.101944_bib0001 article-title: Revisiting the development trajectory of parallel machine scheduling publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2024.106709 – volume: 14 start-page: 539 issue: 3 year: 2023 ident: 10.1016/j.swevo.2025.101944_bib0013 article-title: Joint optimization of production and maintenance scheduling for unrelated parallel machine using hybrid discrete spider monkey optimization algorithm publication-title: Int. J. Ind. Eng. Comput. – volume: 231 year: 2023 ident: 10.1016/j.swevo.2025.101944_bib0043 article-title: Reinforcement learning algorithms: a brief survey publication-title: Expert. Syst. Appl. doi: 10.1016/j.eswa.2023.120495 – volume: 2 year: 2024 ident: 10.1016/j.swevo.2025.101944_bib0008 article-title: Enhancing multi-objective evolutionary algorithms with machine learning for scheduling problems: recent advances and survey publication-title: Front. Ind. Eng. doi: 10.3389/fieng.2024.1337174 – volume: 24 issue: 3 year: 2023 ident: 10.1016/j.swevo.2025.101944_bib0031 article-title: Minimizing tardiness and makespan for distributed heterogeneous unrelated parallel machine scheduling by knowledge and Pareto-based memetic algorithm publication-title: Egyptian Informatics J. doi: 10.1016/j.eij.2023.05.008 – volume: 21 start-page: 9651 issue: 15 year: 2024 ident: 10.1016/j.swevo.2025.101944_bib0053 article-title: An energy-efficient unrelated parallel machine scheduling problem with learning effect of operators and deterioration of jobs publication-title: Int. J. Environ. Sci. Technol. doi: 10.1007/s13762-024-05595-8 – volume: 80 year: 2023 ident: 10.1016/j.swevo.2025.101944_bib0069 article-title: Bi-objective scenario-guided swarm intelligent algorithms based on reinforcement learning for robust unrelated parallel machines scheduling with setup times publication-title: Swarm. Evol. Comput. doi: 10.1016/j.swevo.2023.101321 – volume: 34 start-page: 905 issue: 3 year: 2023 ident: 10.1016/j.swevo.2025.101944_bib0004 article-title: Reinforcement learning applications to machine scheduling problems: a comprehensive literature review publication-title: J. Intell. Manuf. doi: 10.1007/s10845-021-01847-3 – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 10.1016/j.swevo.2025.101944_bib0073 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.996017 – volume: 9 start-page: 56822 year: 2021 ident: 10.1016/j.swevo.2025.101944_bib0033 article-title: A hybrid multi-objective teaching-learning based optimization for scheduling problem of hybrid flow shop with unrelated parallel machine publication-title: IEEe Access. doi: 10.1109/ACCESS.2021.3071729 – ident: 10.1016/j.swevo.2025.101944_bib0011 – volume: 13 start-page: 457 issue: 4 year: 2022 ident: 10.1016/j.swevo.2025.101944_bib0032 article-title: Bi-objective optimization of identical parallel machine scheduling with flexible maintenance and job release times publication-title: Int. J. Ind. Eng. Comput. – volume: 203 year: 2022 ident: 10.1016/j.swevo.2025.101944_bib0074 article-title: A reinforcement learning based RMOEA/D for bi-objective fuzzy flexible job shop scheduling publication-title: Expert. Syst. Appl. doi: 10.1016/j.eswa.2022.117380 – volume: 4 issue: 4 year: 2022 ident: 10.1016/j.swevo.2025.101944_bib0051 article-title: Stochastic parallel machine scheduling using reinforcement learning publication-title: J. Adv. Manuf. Process. doi: 10.1002/amp2.10119 – volume: 21 start-page: 230 issue: 3 year: 2004 ident: 10.1016/j.swevo.2025.101944_bib0003 article-title: A survey of algorithms for single and multi-objective unrelated parallel-machine deterministic scheduling problems publication-title: J. Chin. Inst. Ind. Engineers – volume: 125 start-page: 604 year: 2018 ident: 10.1016/j.swevo.2025.101944_bib0020 article-title: Real-time scheduling for a smart factory using a reinforcement learning approach publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2018.03.039 – volume: 37 start-page: 182 year: 2015 ident: 10.1016/j.swevo.2025.101944_bib0035 article-title: Multi-objective optimization of parallel machine scheduling integrated with multi-resources preventive maintenance planning publication-title: J. Manuf. Syst. doi: 10.1016/j.jmsy.2015.07.002 – volume: 39 start-page: 1315 issue: 7 year: 2012 ident: 10.1016/j.swevo.2025.101944_bib0063 article-title: Minimizing mean weighted tardiness in unrelated parallel machine scheduling with reinforcement learning publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2011.07.019 – volume: 31 start-page: 417 issue: 2 year: 2020 ident: 10.1016/j.swevo.2025.101944_bib0021 article-title: Adaptive job shop scheduling strategy based on weighted Q-learning algorithm publication-title: J. Intell. Manuf. doi: 10.1007/s10845-018-1454-3 |
| SSID | ssj0000602559 |
| Score | 2.3775513 |
| Snippet | •Proposed evolution strategies-based reinforcement learning algorithm.•A multi-agent system to generate scheduling policy for parallel machines.•Action with... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 101944 |
| SubjectTerms | Energy consumption Evolution strategies Multi-agent, reinforcement learning Multi-objective optimization Parallel machine scheduling problem |
| Title | An evolution strategies-based reinforcement learning algorithm for multi-objective dynamic parallel machine scheduling problems |
| URI | https://dx.doi.org/10.1016/j.swevo.2025.101944 |
| Volume | 95 |
| WOSCitedRecordID | wos001476858400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 2210-6502 databaseCode: AIEXJ dateStart: 20110301 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0000602559 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Lb9MwGLdKx4ELb8QYIB-4hUx51vaxmoZgQhPSBiqnyHGc0SpNR5p20y7c-av5_EpfaAIkLlHl1HbS36_-HvoeCL1hhShJHAk_Z2HqJ0kifFYkoR8WALgIeEJ0r8MvH8npKR2N2Kde76fLhVlWpK7p9TW7_K9QwxiArVJn_wLublEYgM8AOlwBdrj-EfDD2pNLu4M3b10pCF_JK5WqokulCu0VdD0jLjxeXcyacfttqsMOdZShP8sn5jT0CtO23lN1wqtKVt5Uh2BKD0xjEFU2o113ppmva7tnV7wxLTi6J1JBekJ3ktgIATiySSJfeTOzsnTdm32yqCfjVZTuYtryGxPjm4-bFTOd7xsW-76wtLcejShdRV4ZN9tOqo06DSOwTX1QJzeObtOfc0cKGIfE5HB-BW93qLZQY8wUmtwqr32mFlbrgi6o6usN7qC9iKSM9tHe8MPx6KTz2AUDbX-pboXuWVwZKx0wuLPb71WdNfXl_CG6b-0OPDR8eYR6sn6MHrieHtge8U_Qj2GNO7DwNn3wBn2wow_u6IPhJt6iD7b0wY4-2NIHr-iDHX2eos_vjs-P3vu2SYcvQFS0_iDgYURImcpIloyzgMcpzYXIwc4XsQB9l6Y8ZKVyeMoQpAMrKSuIjGXC4Q6Nn6F-Pavlc4QDmpNY0oARFe5a5rSgCej3XEoW5oQU-yhyv2cmbAV71Uilylyo4iTTIGQKhMyAsI_edpMuTQGX278-cEBlVgc1umUG5Lpt4ot_nXiA7q3-BC9Rv20W8hW6K5bteN68tiT8BQeUtQQ |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=An+evolution+strategies-based+reinforcement+learning+algorithm+for+multi-objective+dynamic+parallel+machine+scheduling+problems&rft.jtitle=Swarm+and+evolutionary+computation&rft.au=Chen%2C+Yarong&rft.au=Zhang%2C+Junjie&rft.au=Mumtaz%2C+Jabir&rft.au=Huang%2C+Shenquan&rft.date=2025-06-01&rft.pub=Elsevier+B.V&rft.issn=2210-6502&rft.volume=95&rft_id=info:doi/10.1016%2Fj.swevo.2025.101944&rft.externalDocID=S2210650225001026 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2210-6502&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2210-6502&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2210-6502&client=summon |