A K-means-Teaching Learning based optimization algorithm for parallel machine scheduling problem
With the continuous increase of workshop production scale, traditional heuristic algorithms in solving the scheduling problem have the defects of unsatisfactory computing time and insufficient stability of the solution result. However, data mining has a good performance in solving large-scale schedu...
Saved in:
| Published in: | Applied soft computing Vol. 161; p. 111746 |
|---|---|
| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.08.2024
|
| Subjects: | |
| ISSN: | 1568-4946, 1872-9681 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | With the continuous increase of workshop production scale, traditional heuristic algorithms in solving the scheduling problem have the defects of unsatisfactory computing time and insufficient stability of the solution result. However, data mining has a good performance in solving large-scale scheduling problems. A data mining method was proposed for industrial big data to solve the problem of large-scale parallel machines scheduling. This methodology can obtain an effective initial solution for single-operation parallel machine scheduling problem by exploring the effective information in the historical scheduling data. Based on historical customer orders, the offline learning was used to continuously generate simulated data for learning, which makes up for the shortcomings of insufficient data. A TLBO framework (teaching-learning-based optimization) hybrid K-means algorithm was redesigned to enhance the accuracy of offline learning and the efficiency of data searching. In the online operation part, according to the optimal solutions for high-similarity manufacturing orders are the approximate solutions, the new customer order will be quickly matched with the most similar manufacturing order through similarity calculation, and then and then local search is performed. Finally, the globally optimal solution is obtained after screening. Experimental results show that the hybrid teaching–learning methodology can solve the large-scale parallel machines scheduling problem with a better learning performance and computational efficiency.
●A data-driven hybrid learning methodology is proposed, which consists of the offline learning part and the online scheduling part.●A KTLBO algorithm is proposed to learn the optimal scheduling solutions for sample manufacturing orders and historical customer orders in offline learning part.●It can be considered that the optimal solutions for high-similarity manufacturing orders are also approximate in the solution space, an improved K-means algorithm is applied to cluster similar manufacturing orders.●A local search algorithm is proposed to find the global optimal solution of current customer order around the cluster center in the online scheduling part. |
|---|---|
| AbstractList | With the continuous increase of workshop production scale, traditional heuristic algorithms in solving the scheduling problem have the defects of unsatisfactory computing time and insufficient stability of the solution result. However, data mining has a good performance in solving large-scale scheduling problems. A data mining method was proposed for industrial big data to solve the problem of large-scale parallel machines scheduling. This methodology can obtain an effective initial solution for single-operation parallel machine scheduling problem by exploring the effective information in the historical scheduling data. Based on historical customer orders, the offline learning was used to continuously generate simulated data for learning, which makes up for the shortcomings of insufficient data. A TLBO framework (teaching-learning-based optimization) hybrid K-means algorithm was redesigned to enhance the accuracy of offline learning and the efficiency of data searching. In the online operation part, according to the optimal solutions for high-similarity manufacturing orders are the approximate solutions, the new customer order will be quickly matched with the most similar manufacturing order through similarity calculation, and then and then local search is performed. Finally, the globally optimal solution is obtained after screening. Experimental results show that the hybrid teaching–learning methodology can solve the large-scale parallel machines scheduling problem with a better learning performance and computational efficiency.
●A data-driven hybrid learning methodology is proposed, which consists of the offline learning part and the online scheduling part.●A KTLBO algorithm is proposed to learn the optimal scheduling solutions for sample manufacturing orders and historical customer orders in offline learning part.●It can be considered that the optimal solutions for high-similarity manufacturing orders are also approximate in the solution space, an improved K-means algorithm is applied to cluster similar manufacturing orders.●A local search algorithm is proposed to find the global optimal solution of current customer order around the cluster center in the online scheduling part. |
| ArticleNumber | 111746 |
| Author | Liu, Jie Liu, Jinfu Wang, Lei Tang, Hongtao Xu, Wenxiang Guo, Jun Li, Yibing |
| Author_xml | – sequence: 1 givenname: Yibing surname: Li fullname: Li, Yibing organization: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, PR China – sequence: 2 givenname: Jie surname: Liu fullname: Liu, Jie organization: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, PR China – sequence: 3 givenname: Lei orcidid: 0000-0001-9970-9703 surname: Wang fullname: Wang, Lei email: wanglei9455@whut.edu.cn organization: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, PR China – sequence: 4 givenname: Jinfu surname: Liu fullname: Liu, Jinfu organization: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, PR China – sequence: 5 givenname: Hongtao surname: Tang fullname: Tang, Hongtao organization: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, PR China – sequence: 6 givenname: Jun surname: Guo fullname: Guo, Jun organization: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, PR China – sequence: 7 givenname: Wenxiang surname: Xu fullname: Xu, Wenxiang organization: School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang, Hubei 441000, PR China |
| BookMark | eNp90L1OwzAUBWALFYm28AJMeYEE20kcR2KpKv5EJZYymxvnunWVxJEdkODpSVomhk73LN-VzlmQWec6JOSW0YRRJu4OCQSnE055ljDGikxckDmTBY9LIdlszLmQcVZm4oosQjjQEZVczsnHKnqNW4QuxFsEvbfdLtog-G4KFQSsI9cPtrU_MFjXRdDsnLfDvo2M81EPHpoGm6g9UoyC3mP92Uy4965qsL0mlwaagDd_d0neHx-26-d48_b0sl5tYp1SOsSi4FRwJk1ai8yUAgzPESqTloaxSmYZFjXIMi9NygCKXNAUAI2spM64ljpdEn76q70LwaNRvbct-G_FqJo2Ugc1baSmjdRpoxHJf0jb4Vh08GCb8_T-RHEs9WXRq6Atdhpr61EPqnb2HP8FrFyF-Q |
| CitedBy_id | crossref_primary_10_1080_15567036_2025_2519881 crossref_primary_10_2478_amns_2024_2200 crossref_primary_10_3390_systems12090354 crossref_primary_10_2478_amns_2024_2443 crossref_primary_10_1016_j_asoc_2025_113870 crossref_primary_10_1016_j_dajour_2025_100591 crossref_primary_10_3390_su17051841 crossref_primary_10_1080_00207543_2025_2515506 crossref_primary_10_2478_amns_2024_3051 crossref_primary_10_1016_j_suscom_2025_101138 crossref_primary_10_1016_j_tust_2025_106609 crossref_primary_10_1080_17517575_2025_2453244 |
| Cites_doi | 10.1016/j.jfranklin.2007.12.003 10.1007/978-3-642-31600-5_7 10.1016/j.swevo.2021.100996 10.1016/j.ijpe.2005.01.003 10.1016/j.ejor.2009.01.008 10.1016/j.ejor.2011.01.011 10.1016/j.swevo.2022.101143 10.1016/j.ins.2010.10.009 10.1016/j.rcim.2010.12.005 10.4018/IJRSDA.2016010101 10.4028/www.scientific.net/AMR.482-484.2227 10.1016/j.asoc.2022.109980 10.1023/A:1024653810491 10.1177/1687814019838178 10.1016/j.jclepro.2018.11.231 10.1007/978-3-642-13495-1_69 10.1016/j.engappai.2012.04.001 10.1177/1687814018801442 10.1016/j.rcim.2020.102081 10.1080/18756891.2015.1017383 10.1007/s10951-020-00664-5 10.1021/ie800124g 10.3233/JIFS-169389 10.1016/j.cie.2016.06.025 10.1137/1.9781611975994.168 10.1080/0951192X.2013.800229 10.1016/j.apm.2013.07.038 10.1080/00207543.2013.802389 10.1007/s10100-018-0553-8 10.1016/j.swevo.2018.01.012 10.1109/TSMC.2018.2855700 10.24846/v24i2y201505 10.1080/00207543.2011.627388 10.1016/j.swevo.2023.101233 10.1016/j.swevo.2023.101316 10.1016/j.eswa.2010.08.145 10.1016/j.cie.2020.106347 10.1007/s10951-012-0270-4 10.4028/www.scientific.net/AMR.845.559 10.1109/TSMC.2021.3120702 10.1016/S0360-8352(00)00050-4 10.1007/978-3-319-13731-5_19 10.1007/s00170-016-9866-8 10.1016/j.ijpe.2004.12.007 10.1016/j.eswa.2020.113721 10.1016/j.eswa.2010.12.101 10.1109/TSMC.2016.2616347 10.1109/DMO.2009.5341895 10.1007/s12652-016-0425-9 10.1016/j.neucom.2013.10.042 10.1186/s13677-022-00282-w |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier B.V. |
| Copyright_xml | – notice: 2024 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.asoc.2024.111746 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-9681 |
| ExternalDocumentID | 10_1016_j_asoc_2024_111746 S1568494624005209 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 23M 4.4 457 4G. 53G 5GY 5VS 6J9 7-5 71M 8P~ AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABFNM ABFRF ABJNI ABMAC ABXDB ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 EBS EFJIC EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HVGLF HZ~ IHE J1W JJJVA KOM M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SDF SDG SES SEW SPC SPCBC SST SSV SSZ T5K UHS UNMZH ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP CITATION EFKBS EFLBG ~HD |
| ID | FETCH-LOGICAL-c300t-67206218f3d64f96af25eabf39f11b844e7da8959f31aa75603aaef8b8c42c8c3 |
| ISICitedReferencesCount | 11 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001246725200002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1568-4946 |
| IngestDate | Sat Nov 29 03:06:03 EST 2025 Tue Nov 18 21:42:03 EST 2025 Tue Jun 18 08:51:32 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Teaching-Learning-Based Optimization Algorithm K-means algorithm Data Mining Offline learning Parallel machine scheduling |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-67206218f3d64f96af25eabf39f11b844e7da8959f31aa75603aaef8b8c42c8c3 |
| ORCID | 0000-0001-9970-9703 |
| ParticipantIDs | crossref_primary_10_1016_j_asoc_2024_111746 crossref_citationtrail_10_1016_j_asoc_2024_111746 elsevier_sciencedirect_doi_10_1016_j_asoc_2024_111746 |
| PublicationCentury | 2000 |
| PublicationDate | August 2024 2024-08-00 |
| PublicationDateYYYYMMDD | 2024-08-01 |
| PublicationDate_xml | – month: 08 year: 2024 text: August 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | Applied soft computing |
| PublicationYear | 2024 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Wang, Guo, Li, Du, Xu (bib1) 2016; 94 P.K. Mummareddy, S.C. Satapaty, - An Hybrid Approach for Data Clustering Using K-Means and Teaching Learning Based Optimization, (2015) - 171. Toader (bib25) 2015; 24 Shivasankaran, Kumar, Raja (bib26) 2015; 8 Vallada, Ruiz (bib53) 2011; 211 Luo, Deng, Gong, Zhang, Han, Li (bib3) 2020; 160 Koonce, Tsai (bib46) 2000; 38 Zheng, Wang (bib21) 2018; 48 Habib Zahmani, Atmani (bib39) 2020; 24 R. Ismail, Z. Othman, A.Abu Bakar, Data Mining In Production Planning and Scheduling: A Review, in: 2nd Conference on Data Mining and Optimization, Ieee Computer Soc, Bangi, MALAYSIA, 2009, pp. 159-164. Nhu Binh, Cing (bib30) 2008; 38 Liu, Huang (bib42) 2023 Dudas, Frantzén, Ng (bib6) 2011; 27 Song, Ou, Wu, Wu, Xing, Chen (bib48) 2023; 79 Tahar, Yalaoui, Chu, Amodeo (bib11) 2006; 99 Bülbül, Kaminsky (bib10) 2012; 16 Wang, Pan, Gao, Wang (bib32) 2022; 74 Li, Pan, Tasgetiren (bib19) 2014; 38 A. Antoniadis, N. Garg, G. Kumar, N. Kumar, Acm, Parallel Machine Scheduling to Minimize Energy Consumption, in: 31st Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), Assoc Computing Machinery, Salt Lake City, UT, 2020, pp. 2758-2769. Kanungo, Nayak, Naik, Behera (bib50) 2016; 3 Huang, Guan, Yang (bib27) 2018; 10 Pan, Lei, Wang (bib36) 2022; 52 J.C. Tang, G.J. Zhang, B.B. Lin, B.X. Zhang, A Hybrid PSO/GA Algorithm for Job Shop Scheduling Problem, in: 1st International Conference on Swarm Intelligence, Springer-Verlag Berlin, Beijing, PEOPLES R CHINA, 2010, pp. 566-+. Sawik (bib15) 2012; 50 Liang, Huang, Ning (bib28) 2016; 9 Ozcan, Yavuz, Fıglalı (bib40) 2020; 0 Asadzadeh (bib20) 2016; 102 Huang, Chen, Zhang, Su, Lin, Cao (bib55) 2022; 11 Bautista-Valhondo, Alfaro-Pozo (bib12) 2018; 28 Wang, Zhang, Choi (bib29) 2020; 50 Wu (bib8) 2013; 27 Zhang, Gao, Shi (bib17) 2011; 38 Wang, Ren, Bai, Ezeh, Zhang, Dong (bib56) 2022; 69 Mencía, Sierra, Varela (bib24) 2013; 51 Bartz-Beielstein, Preuss (bib54) 2014 Piroozfard, Hassan, Moghadam, Derakhshan Asl (bib33) 2014; 845 Javadi, Saidi-Mehrabad, Haji, Mahdavi, Jolai, Mahdavi-Amiri (bib14) 2008; 345 De Giovanni, Pezzella (bib16) 2010; 200 Jian, Nee, Fuh, Zhang (bib18) 2003; 14 R. Balasundaram, N. Baskar, R.S. Sankar, Discovering Dispathcing Rules for Job Shop Schdeuling Using Data Mining, in: 2nd International Conference on Advances in Computing and Information Technology (ACITY 2012), Springer-Verlag Berlin, Chennai, INDIA, 2012, pp. 63-+. Qiu, Sawhney, Zhang, Chen, Zhang, Lisar, Jiang, Ji (bib47) 2019; 11 Xu, Li, Li (bib49) 2023; 134 Liu, Guo, Wang (bib2) 2019; 211 Wu, Chen, Lin, Lai, Liu, Yu (bib23) 2018; 41 Czuczai, Farkas, Rev, Lelkes (bib9) 2009; 48 Shahzad, Mebarki (bib44) 2012; 25 Shang, Tian, Liu, Liu (bib34) 2018; 34 Yan, Shi, Zhao (bib37) 2012; 482-484 Khademi Zare, Fakhrzad (bib38) 2011; 38 Meng, Zhang, Ren, Zhang, Lv (bib13) 2020; 142 Gajpal, Rajendran (bib22) 2006; 101 Ou, Xing, Yao, Li, Lv, He, Song, Wu, Zhang (bib41) 2023; 77 Zhang, Tan, Peng, Gao, Shen, Lian (bib5) 2021; 68 Pan, Wang, Gao, Li (bib35) 2011; 181 Liu, Tseng, Huang, Wang (bib45) 2023 Xu, Wang, Wang, Liu (bib52) 2015; 148 Asadzadeh (10.1016/j.asoc.2024.111746_bib20) 2016; 102 Wang (10.1016/j.asoc.2024.111746_bib56) 2022; 69 Liu (10.1016/j.asoc.2024.111746_bib45) 2023 Liang (10.1016/j.asoc.2024.111746_bib28) 2016; 9 Bülbül (10.1016/j.asoc.2024.111746_bib10) 2012; 16 10.1016/j.asoc.2024.111746_bib31 Vallada (10.1016/j.asoc.2024.111746_bib53) 2011; 211 Xu (10.1016/j.asoc.2024.111746_bib49) 2023; 134 Wang (10.1016/j.asoc.2024.111746_bib29) 2020; 50 Pan (10.1016/j.asoc.2024.111746_bib36) 2022; 52 Huang (10.1016/j.asoc.2024.111746_bib27) 2018; 10 Kanungo (10.1016/j.asoc.2024.111746_bib50) 2016; 3 De Giovanni (10.1016/j.asoc.2024.111746_bib16) 2010; 200 Zhang (10.1016/j.asoc.2024.111746_bib5) 2021; 68 Qiu (10.1016/j.asoc.2024.111746_bib47) 2019; 11 Song (10.1016/j.asoc.2024.111746_bib48) 2023; 79 Czuczai (10.1016/j.asoc.2024.111746_bib9) 2009; 48 10.1016/j.asoc.2024.111746_bib43 Huang (10.1016/j.asoc.2024.111746_bib55) 2022; 11 10.1016/j.asoc.2024.111746_bib4 Shahzad (10.1016/j.asoc.2024.111746_bib44) 2012; 25 10.1016/j.asoc.2024.111746_bib7 Habib Zahmani (10.1016/j.asoc.2024.111746_bib39) 2020; 24 Meng (10.1016/j.asoc.2024.111746_bib13) 2020; 142 Bautista-Valhondo (10.1016/j.asoc.2024.111746_bib12) 2018; 28 Piroozfard (10.1016/j.asoc.2024.111746_bib33) 2014; 845 Wang (10.1016/j.asoc.2024.111746_bib1) 2016; 94 Nhu Binh (10.1016/j.asoc.2024.111746_bib30) 2008; 38 Zhang (10.1016/j.asoc.2024.111746_bib17) 2011; 38 Luo (10.1016/j.asoc.2024.111746_bib3) 2020; 160 Xu (10.1016/j.asoc.2024.111746_bib52) 2015; 148 Wang (10.1016/j.asoc.2024.111746_bib32) 2022; 74 Gajpal (10.1016/j.asoc.2024.111746_bib22) 2006; 101 Ozcan (10.1016/j.asoc.2024.111746_bib40) 2020; 0 Yan (10.1016/j.asoc.2024.111746_bib37) 2012; 482-484 Wu (10.1016/j.asoc.2024.111746_bib8) 2013; 27 Tahar (10.1016/j.asoc.2024.111746_bib11) 2006; 99 Javadi (10.1016/j.asoc.2024.111746_bib14) 2008; 345 Mencía (10.1016/j.asoc.2024.111746_bib24) 2013; 51 Toader (10.1016/j.asoc.2024.111746_bib25) 2015; 24 Bartz-Beielstein (10.1016/j.asoc.2024.111746_bib54) 2014 Shang (10.1016/j.asoc.2024.111746_bib34) 2018; 34 Ou (10.1016/j.asoc.2024.111746_bib41) 2023; 77 10.1016/j.asoc.2024.111746_bib51 Shivasankaran (10.1016/j.asoc.2024.111746_bib26) 2015; 8 Dudas (10.1016/j.asoc.2024.111746_bib6) 2011; 27 Liu (10.1016/j.asoc.2024.111746_bib42) 2023 Zheng (10.1016/j.asoc.2024.111746_bib21) 2018; 48 Pan (10.1016/j.asoc.2024.111746_bib35) 2011; 181 Wu (10.1016/j.asoc.2024.111746_bib23) 2018; 41 Khademi Zare (10.1016/j.asoc.2024.111746_bib38) 2011; 38 Liu (10.1016/j.asoc.2024.111746_bib2) 2019; 211 Koonce (10.1016/j.asoc.2024.111746_bib46) 2000; 38 Sawik (10.1016/j.asoc.2024.111746_bib15) 2012; 50 Jian (10.1016/j.asoc.2024.111746_bib18) 2003; 14 Li (10.1016/j.asoc.2024.111746_bib19) 2014; 38 |
| References_xml | – volume: 38 start-page: 1111 year: 2014 end-page: 1132 ident: bib19 article-title: A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities publication-title: Appl. Math. Model. – volume: 68 year: 2021 ident: bib5 article-title: A discrete whale swarm algorithm for hybrid flow-shop scheduling problem with limited buffers publication-title: Robot. Comput. -Integr. Manuf. – volume: 27 start-page: 20 year: 2013 end-page: 35 ident: bib8 article-title: Solving a two-agent single-machine learning scheduling problem publication-title: Int. J. Comput. Integr. Manuf. – volume: 48 start-page: 790 year: 2018 end-page: 800 ident: bib21 article-title: A collaborative multiobjective fruit fly optimization algorithm for the resource constrained unrelated parallel machine green scheduling problem publication-title: IEEE Trans. Syst., Man, Cybern.: Syst. – volume: 24 start-page: 171 year: 2015 end-page: 180 ident: bib25 article-title: A hybrid algorithm for job shop scheduling problem publication-title: Stud. Inform. Control – reference: R. Balasundaram, N. Baskar, R.S. Sankar, Discovering Dispathcing Rules for Job Shop Schdeuling Using Data Mining, in: 2nd International Conference on Advances in Computing and Information Technology (ACITY 2012), Springer-Verlag Berlin, Chennai, INDIA, 2012, pp. 63-+. – volume: 14 start-page: 351 year: 2003 end-page: 362 ident: bib18 article-title: A modified genetic algorithm for distributed scheduling problems publication-title: J. Intell. Manuf. – reference: J.C. Tang, G.J. Zhang, B.B. Lin, B.X. Zhang, A Hybrid PSO/GA Algorithm for Job Shop Scheduling Problem, in: 1st International Conference on Swarm Intelligence, Springer-Verlag Berlin, Beijing, PEOPLES R CHINA, 2010, pp. 566-+. – volume: 845 start-page: 559 year: 2014 end-page: 563 ident: bib33 article-title: A hybrid genetic algorithm for solving job shop scheduling problems publication-title: Adv. Mater. Res. – volume: 50 start-page: 4598 year: 2020 end-page: 4610 ident: bib29 article-title: Bi-objective optimal scheduling with raw material’s shelf-life constraints in unrelated parallel machines production publication-title: IEEE Trans. Syst., Man, Cybern.: Syst. – volume: 8 start-page: 455 year: 2015 end-page: 466 ident: bib26 article-title: Hybrid sorting immune simulated annealing algorithm for flexible job shop scheduling publication-title: Int. J. Comput. Intell. Syst. – volume: 16 start-page: 161 year: 2012 end-page: 183 ident: bib10 article-title: A linear programming-based method for job shop scheduling publication-title: J. Sched. – volume: 48 start-page: 5321 year: 2009 end-page: 5335 ident: bib9 article-title: New mixed integer linear programming model for solving scheduling problems with special characteristics publication-title: Ind. Eng. Chem. Res. – volume: 94 start-page: 3375 year: 2016 end-page: 3388 ident: bib1 article-title: Distributed manufacturing resource selection strategy in cloud manufacturing publication-title: Int. J. Adv. Manuf. Technol. – volume: 69 year: 2022 ident: bib56 article-title: Minimizing the sum of makespan on multi-agent single-machine scheduling with release dates publication-title: Swarm Evol. Comput. – volume: 38 start-page: 361 year: 2000 end-page: 374 ident: bib46 article-title: Using data mining to find patterns in genetic algorithm solutions to a job shop schedule publication-title: Comput. Ind. Eng. – volume: 181 start-page: 668 year: 2011 end-page: 685 ident: bib35 article-title: An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers publication-title: Inf. Sci. – volume: 74 year: 2022 ident: bib32 article-title: An effective two-stage iterated greedy algorithm to minimize total tardiness for the distributed flowshop group scheduling problem publication-title: Swarm Evol. Comput. – volume: 160 year: 2020 ident: bib3 article-title: An efficient memetic algorithm for distributed flexible job shop scheduling problem with transfers publication-title: Expert Syst. Appl. – volume: 211 start-page: 612 year: 2011 end-page: 622 ident: bib53 article-title: A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times publication-title: Eur. J. Oper. Res. – volume: 200 start-page: 395 year: 2010 end-page: 408 ident: bib16 article-title: An improved genetic algorithm for the distributed and flexible job-shop scheduling problem publication-title: Eur. J. Oper. Res. – volume: 11 start-page: 14 year: 2019 ident: bib47 article-title: Data mining-based disturbances prediction for job shop scheduling publication-title: Adv. Mech. Eng. – volume: 27 start-page: 687 year: 2011 end-page: 695 ident: bib6 article-title: A synergy of multi-objective optimization and data mining for the analysis of a flexible flow shop publication-title: Robot. Comput. -Integr. Manuf. – volume: 99 start-page: 63 year: 2006 end-page: 73 ident: bib11 article-title: A linear programming approach for identical parallel machine scheduling with job splitting and sequence-dependent setup times publication-title: Int. J. Prod. Econ. – reference: A. Antoniadis, N. Garg, G. Kumar, N. Kumar, Acm, Parallel Machine Scheduling to Minimize Energy Consumption, in: 31st Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), Assoc Computing Machinery, Salt Lake City, UT, 2020, pp. 2758-2769. – volume: 11 year: 2022 ident: bib55 article-title: Improved firefly algorithm with courtship learning for unrelated parallel machine scheduling problem with sequence-dependent setup times publication-title: J. Cloud Comput. – start-page: 1 year: 2023 end-page: 13 ident: bib45 article-title: Dynamic parallel machine scheduling with deep q-network publication-title: IEEE Trans. Syst., Man, Cybern.: Syst. – volume: 10 year: 2018 ident: bib27 article-title: An effective hybrid algorithm for multi-objective flexible job-shop scheduling problem publication-title: Adv. Mech. Eng. – volume: 34 start-page: 955 year: 2018 end-page: 964 ident: bib34 article-title: Production scheduling optimization method based on hybrid particle swarm optimization algorithm publication-title: J. Intell. Fuzzy Syst. – reference: R. Ismail, Z. Othman, A.Abu Bakar, Data Mining In Production Planning and Scheduling: A Review, in: 2nd Conference on Data Mining and Optimization, Ieee Computer Soc, Bangi, MALAYSIA, 2009, pp. 159-164. – volume: 51 start-page: 5221 year: 2013 end-page: 5237 ident: bib24 article-title: An efficient hybrid search algorithm for job shop scheduling with operators publication-title: Int. J. Prod. Res. – year: 2014 ident: bib54 article-title: Experimental methods for the analysis of optimization algorithms publication-title: Int. J. Behav. Nutr. Phys. Act., ( – volume: 101 start-page: 259 year: 2006 end-page: 272 ident: bib22 article-title: An ant-colony optimization algorithm for minimizing the completion-time variance of jobs in flowshops publication-title: Int. J. Prod. Econ. – start-page: 1 year: 2023 end-page: 13 ident: bib42 article-title: Dynamic job-shop scheduling problems using graph neural network and deep reinforcement learning publication-title: IEEE Trans. Syst., Man, Cybern.: Syst. – volume: 148 start-page: 260 year: 2015 end-page: 268 ident: bib52 article-title: An effective teaching–learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time publication-title: Neurocomputing – volume: 211 start-page: 765 year: 2019 end-page: 786 ident: bib2 article-title: Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption publication-title: J. Clean. Prod. – volume: 142 year: 2020 ident: bib13 article-title: Mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem publication-title: Comput. Ind. Eng. – volume: 41 start-page: 97 year: 2018 end-page: 110 ident: bib23 article-title: A two-stage three-machine assembly flow shop scheduling with learning consideration to minimize the flowtime by six hybrids of particle swarm optimization publication-title: Swarm Evol. Comput. – volume: 134 year: 2023 ident: bib49 article-title: A discrete teaching–learning based optimization algorithm with local search for rescue task allocation and scheduling publication-title: Appl. Soft. Comput. – volume: 25 start-page: 1173 year: 2012 end-page: 1181 ident: bib44 article-title: Data mining based job dispatching using hybrid simulation-optimization approach for shop scheduling problem publication-title: Eng. Appl. Artif. Intell. – volume: 79 year: 2023 ident: bib48 article-title: A cluster-based genetic optimization method for satellite range scheduling system publication-title: Swarm Evol. Comput. – volume: 3 start-page: 1 year: 2016 end-page: 19 ident: bib50 article-title: Hybrid clustering using elitist teaching learning-based optimization publication-title: Int. J. Rough. Sets Data Anal. – reference: P.K. Mummareddy, S.C. Satapaty, - An Hybrid Approach for Data Clustering Using K-Means and Teaching Learning Based Optimization, (2015) - 171. – volume: 482-484 start-page: 2227 year: 2012 end-page: 2233 ident: bib37 article-title: Research on parallel hybrid genetic algorithm based on multi-group in job shop scheduling publication-title: Adv. Mater. Res. – volume: 38 start-page: 7609 year: 2011 end-page: 7615 ident: bib38 article-title: Solving flexible flow-shop problem with a hybrid genetic algorithm and data mining: a fuzzy approach publication-title: Expert Syst. Appl. – volume: 24 start-page: 175 year: 2020 end-page: 196 ident: bib39 article-title: Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation publication-title: J. Sched. – volume: 345 start-page: 452 year: 2008 end-page: 467 ident: bib14 article-title: No-wait flow shop scheduling using fuzzy multi-objective linear programming publication-title: J. Frankl. Inst. – volume: 52 start-page: 5295 year: 2022 end-page: 5307 ident: bib36 article-title: A bi-population evolutionary algorithm with feedback for energy-efficient fuzzy flexible job shop scheduling publication-title: IEEE Trans. Syst., Man, Cybern.: Syst. – volume: 38 start-page: 674 year: 2008 end-page: 685 ident: bib30 article-title: Solving Multiple-Objective Flexible Job Shop Problems by Evolution and Local Search, IEEE Transactions on Systems, Man, and Cybernetics publication-title: Part C. (Appl. Rev. ) – volume: 9 start-page: 165 year: 2016 end-page: 171 ident: bib28 article-title: Flexible job shop scheduling based on improved hybrid immune algorithm publication-title: J. Ambient Intell. Humaniz. Comput. – volume: 38 start-page: 3563 year: 2011 end-page: 3573 ident: bib17 article-title: An effective genetic algorithm for the flexible job-shop scheduling problem publication-title: Expert Syst. Appl. – volume: 28 start-page: 5 year: 2018 end-page: 23 ident: bib12 article-title: Mixed integer linear programming models for Flow Shop Scheduling with a demand plan of job types publication-title: Cent. Eur. J. Oper. Res. – volume: 77 year: 2023 ident: bib41 article-title: Deep reinforcement learning method for satellite range scheduling problem publication-title: Swarm Evol. Comput. – volume: 0 year: 2020 ident: bib40 article-title: A data mining based solution method for flow shop scheduling problems publication-title: Sci. Iran. – volume: 50 start-page: 5017 year: 2012 end-page: 5034 ident: bib15 article-title: Batch versus cyclic scheduling of flexible flow shops by mixed-integer programming publication-title: Int. J. Prod. Res. – volume: 102 start-page: 359 year: 2016 end-page: 367 ident: bib20 article-title: A parallel artificial bee colony algorithm for the job shop scheduling problem with a dynamic migration strategy publication-title: Comput. Ind. Eng. – volume: 345 start-page: 452 year: 2008 ident: 10.1016/j.asoc.2024.111746_bib14 article-title: No-wait flow shop scheduling using fuzzy multi-objective linear programming publication-title: J. Frankl. Inst. doi: 10.1016/j.jfranklin.2007.12.003 – ident: 10.1016/j.asoc.2024.111746_bib43 doi: 10.1007/978-3-642-31600-5_7 – volume: 69 year: 2022 ident: 10.1016/j.asoc.2024.111746_bib56 article-title: Minimizing the sum of makespan on multi-agent single-machine scheduling with release dates publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2021.100996 – volume: 101 start-page: 259 year: 2006 ident: 10.1016/j.asoc.2024.111746_bib22 article-title: An ant-colony optimization algorithm for minimizing the completion-time variance of jobs in flowshops publication-title: Int. J. Prod. Econ. doi: 10.1016/j.ijpe.2005.01.003 – volume: 200 start-page: 395 year: 2010 ident: 10.1016/j.asoc.2024.111746_bib16 article-title: An improved genetic algorithm for the distributed and flexible job-shop scheduling problem publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2009.01.008 – volume: 211 start-page: 612 year: 2011 ident: 10.1016/j.asoc.2024.111746_bib53 article-title: A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2011.01.011 – volume: 74 year: 2022 ident: 10.1016/j.asoc.2024.111746_bib32 article-title: An effective two-stage iterated greedy algorithm to minimize total tardiness for the distributed flowshop group scheduling problem publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2022.101143 – volume: 181 start-page: 668 year: 2011 ident: 10.1016/j.asoc.2024.111746_bib35 article-title: An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers publication-title: Inf. Sci. doi: 10.1016/j.ins.2010.10.009 – volume: 27 start-page: 687 year: 2011 ident: 10.1016/j.asoc.2024.111746_bib6 article-title: A synergy of multi-objective optimization and data mining for the analysis of a flexible flow shop publication-title: Robot. Comput. -Integr. Manuf. doi: 10.1016/j.rcim.2010.12.005 – volume: 3 start-page: 1 year: 2016 ident: 10.1016/j.asoc.2024.111746_bib50 article-title: Hybrid clustering using elitist teaching learning-based optimization publication-title: Int. J. Rough. Sets Data Anal. doi: 10.4018/IJRSDA.2016010101 – volume: 482-484 start-page: 2227 year: 2012 ident: 10.1016/j.asoc.2024.111746_bib37 article-title: Research on parallel hybrid genetic algorithm based on multi-group in job shop scheduling publication-title: Adv. Mater. Res. doi: 10.4028/www.scientific.net/AMR.482-484.2227 – volume: 134 year: 2023 ident: 10.1016/j.asoc.2024.111746_bib49 article-title: A discrete teaching–learning based optimization algorithm with local search for rescue task allocation and scheduling publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2022.109980 – volume: 14 start-page: 351 year: 2003 ident: 10.1016/j.asoc.2024.111746_bib18 article-title: A modified genetic algorithm for distributed scheduling problems publication-title: J. Intell. Manuf. doi: 10.1023/A:1024653810491 – volume: 0 year: 2020 ident: 10.1016/j.asoc.2024.111746_bib40 article-title: A data mining based solution method for flow shop scheduling problems publication-title: Sci. Iran. – volume: 11 start-page: 14 year: 2019 ident: 10.1016/j.asoc.2024.111746_bib47 article-title: Data mining-based disturbances prediction for job shop scheduling publication-title: Adv. Mech. Eng. doi: 10.1177/1687814019838178 – volume: 211 start-page: 765 year: 2019 ident: 10.1016/j.asoc.2024.111746_bib2 article-title: Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2018.11.231 – ident: 10.1016/j.asoc.2024.111746_bib31 doi: 10.1007/978-3-642-13495-1_69 – volume: 25 start-page: 1173 year: 2012 ident: 10.1016/j.asoc.2024.111746_bib44 article-title: Data mining based job dispatching using hybrid simulation-optimization approach for shop scheduling problem publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2012.04.001 – volume: 10 year: 2018 ident: 10.1016/j.asoc.2024.111746_bib27 article-title: An effective hybrid algorithm for multi-objective flexible job-shop scheduling problem publication-title: Adv. Mech. Eng. doi: 10.1177/1687814018801442 – volume: 38 start-page: 674 year: 2008 ident: 10.1016/j.asoc.2024.111746_bib30 article-title: Solving Multiple-Objective Flexible Job Shop Problems by Evolution and Local Search, IEEE Transactions on Systems, Man, and Cybernetics publication-title: Part C. (Appl. Rev. ) – volume: 68 year: 2021 ident: 10.1016/j.asoc.2024.111746_bib5 article-title: A discrete whale swarm algorithm for hybrid flow-shop scheduling problem with limited buffers publication-title: Robot. Comput. -Integr. Manuf. doi: 10.1016/j.rcim.2020.102081 – volume: 8 start-page: 455 year: 2015 ident: 10.1016/j.asoc.2024.111746_bib26 article-title: Hybrid sorting immune simulated annealing algorithm for flexible job shop scheduling publication-title: Int. J. Comput. Intell. Syst. doi: 10.1080/18756891.2015.1017383 – volume: 24 start-page: 175 year: 2020 ident: 10.1016/j.asoc.2024.111746_bib39 article-title: Multiple dispatching rules allocation in real time using data mining, genetic algorithms, and simulation publication-title: J. Sched. doi: 10.1007/s10951-020-00664-5 – volume: 48 start-page: 5321 year: 2009 ident: 10.1016/j.asoc.2024.111746_bib9 article-title: New mixed integer linear programming model for solving scheduling problems with special characteristics publication-title: Ind. Eng. Chem. Res. doi: 10.1021/ie800124g – volume: 34 start-page: 955 year: 2018 ident: 10.1016/j.asoc.2024.111746_bib34 article-title: Production scheduling optimization method based on hybrid particle swarm optimization algorithm publication-title: J. Intell. Fuzzy Syst. doi: 10.3233/JIFS-169389 – volume: 102 start-page: 359 year: 2016 ident: 10.1016/j.asoc.2024.111746_bib20 article-title: A parallel artificial bee colony algorithm for the job shop scheduling problem with a dynamic migration strategy publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2016.06.025 – ident: 10.1016/j.asoc.2024.111746_bib4 doi: 10.1137/1.9781611975994.168 – volume: 27 start-page: 20 year: 2013 ident: 10.1016/j.asoc.2024.111746_bib8 article-title: Solving a two-agent single-machine learning scheduling problem publication-title: Int. J. Comput. Integr. Manuf. doi: 10.1080/0951192X.2013.800229 – volume: 38 start-page: 1111 year: 2014 ident: 10.1016/j.asoc.2024.111746_bib19 article-title: A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities publication-title: Appl. Math. Model. doi: 10.1016/j.apm.2013.07.038 – volume: 51 start-page: 5221 year: 2013 ident: 10.1016/j.asoc.2024.111746_bib24 article-title: An efficient hybrid search algorithm for job shop scheduling with operators publication-title: Int. J. Prod. Res. doi: 10.1080/00207543.2013.802389 – volume: 28 start-page: 5 year: 2018 ident: 10.1016/j.asoc.2024.111746_bib12 article-title: Mixed integer linear programming models for Flow Shop Scheduling with a demand plan of job types publication-title: Cent. Eur. J. Oper. Res. doi: 10.1007/s10100-018-0553-8 – volume: 41 start-page: 97 year: 2018 ident: 10.1016/j.asoc.2024.111746_bib23 article-title: A two-stage three-machine assembly flow shop scheduling with learning consideration to minimize the flowtime by six hybrids of particle swarm optimization publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2018.01.012 – volume: 50 start-page: 4598 year: 2020 ident: 10.1016/j.asoc.2024.111746_bib29 article-title: Bi-objective optimal scheduling with raw material’s shelf-life constraints in unrelated parallel machines production publication-title: IEEE Trans. Syst., Man, Cybern.: Syst. doi: 10.1109/TSMC.2018.2855700 – volume: 24 start-page: 171 year: 2015 ident: 10.1016/j.asoc.2024.111746_bib25 article-title: A hybrid algorithm for job shop scheduling problem publication-title: Stud. Inform. Control doi: 10.24846/v24i2y201505 – volume: 50 start-page: 5017 year: 2012 ident: 10.1016/j.asoc.2024.111746_bib15 article-title: Batch versus cyclic scheduling of flexible flow shops by mixed-integer programming publication-title: Int. J. Prod. Res. doi: 10.1080/00207543.2011.627388 – volume: 77 year: 2023 ident: 10.1016/j.asoc.2024.111746_bib41 article-title: Deep reinforcement learning method for satellite range scheduling problem publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2023.101233 – year: 2014 ident: 10.1016/j.asoc.2024.111746_bib54 article-title: Experimental methods for the analysis of optimization algorithms publication-title: Int. J. Behav. Nutr. Phys. Act., ( – volume: 79 year: 2023 ident: 10.1016/j.asoc.2024.111746_bib48 article-title: A cluster-based genetic optimization method for satellite range scheduling system publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2023.101316 – volume: 38 start-page: 3563 year: 2011 ident: 10.1016/j.asoc.2024.111746_bib17 article-title: An effective genetic algorithm for the flexible job-shop scheduling problem publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2010.08.145 – volume: 142 year: 2020 ident: 10.1016/j.asoc.2024.111746_bib13 article-title: Mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2020.106347 – volume: 16 start-page: 161 year: 2012 ident: 10.1016/j.asoc.2024.111746_bib10 article-title: A linear programming-based method for job shop scheduling publication-title: J. Sched. doi: 10.1007/s10951-012-0270-4 – volume: 845 start-page: 559 year: 2014 ident: 10.1016/j.asoc.2024.111746_bib33 article-title: A hybrid genetic algorithm for solving job shop scheduling problems publication-title: Adv. Mater. Res. doi: 10.4028/www.scientific.net/AMR.845.559 – volume: 52 start-page: 5295 year: 2022 ident: 10.1016/j.asoc.2024.111746_bib36 article-title: A bi-population evolutionary algorithm with feedback for energy-efficient fuzzy flexible job shop scheduling publication-title: IEEE Trans. Syst., Man, Cybern.: Syst. doi: 10.1109/TSMC.2021.3120702 – volume: 38 start-page: 361 year: 2000 ident: 10.1016/j.asoc.2024.111746_bib46 article-title: Using data mining to find patterns in genetic algorithm solutions to a job shop schedule publication-title: Comput. Ind. Eng. doi: 10.1016/S0360-8352(00)00050-4 – ident: 10.1016/j.asoc.2024.111746_bib51 doi: 10.1007/978-3-319-13731-5_19 – volume: 94 start-page: 3375 year: 2016 ident: 10.1016/j.asoc.2024.111746_bib1 article-title: Distributed manufacturing resource selection strategy in cloud manufacturing publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-016-9866-8 – volume: 99 start-page: 63 year: 2006 ident: 10.1016/j.asoc.2024.111746_bib11 article-title: A linear programming approach for identical parallel machine scheduling with job splitting and sequence-dependent setup times publication-title: Int. J. Prod. Econ. doi: 10.1016/j.ijpe.2004.12.007 – start-page: 1 year: 2023 ident: 10.1016/j.asoc.2024.111746_bib45 article-title: Dynamic parallel machine scheduling with deep q-network publication-title: IEEE Trans. Syst., Man, Cybern.: Syst. – volume: 160 year: 2020 ident: 10.1016/j.asoc.2024.111746_bib3 article-title: An efficient memetic algorithm for distributed flexible job shop scheduling problem with transfers publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2020.113721 – volume: 38 start-page: 7609 year: 2011 ident: 10.1016/j.asoc.2024.111746_bib38 article-title: Solving flexible flow-shop problem with a hybrid genetic algorithm and data mining: a fuzzy approach publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2010.12.101 – volume: 48 start-page: 790 year: 2018 ident: 10.1016/j.asoc.2024.111746_bib21 article-title: A collaborative multiobjective fruit fly optimization algorithm for the resource constrained unrelated parallel machine green scheduling problem publication-title: IEEE Trans. Syst., Man, Cybern.: Syst. doi: 10.1109/TSMC.2016.2616347 – ident: 10.1016/j.asoc.2024.111746_bib7 doi: 10.1109/DMO.2009.5341895 – volume: 9 start-page: 165 year: 2016 ident: 10.1016/j.asoc.2024.111746_bib28 article-title: Flexible job shop scheduling based on improved hybrid immune algorithm publication-title: J. Ambient Intell. Humaniz. Comput. doi: 10.1007/s12652-016-0425-9 – volume: 148 start-page: 260 year: 2015 ident: 10.1016/j.asoc.2024.111746_bib52 article-title: An effective teaching–learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.10.042 – start-page: 1 year: 2023 ident: 10.1016/j.asoc.2024.111746_bib42 article-title: Dynamic job-shop scheduling problems using graph neural network and deep reinforcement learning publication-title: IEEE Trans. Syst., Man, Cybern.: Syst. – volume: 11 year: 2022 ident: 10.1016/j.asoc.2024.111746_bib55 article-title: Improved firefly algorithm with courtship learning for unrelated parallel machine scheduling problem with sequence-dependent setup times publication-title: J. Cloud Comput. doi: 10.1186/s13677-022-00282-w |
| SSID | ssj0016928 |
| Score | 2.50211 |
| Snippet | With the continuous increase of workshop production scale, traditional heuristic algorithms in solving the scheduling problem have the defects of... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 111746 |
| SubjectTerms | Data Mining K-means algorithm Offline learning Parallel machine scheduling Teaching-Learning-Based Optimization Algorithm |
| Title | A K-means-Teaching Learning based optimization algorithm for parallel machine scheduling problem |
| URI | https://dx.doi.org/10.1016/j.asoc.2024.111746 |
| Volume | 161 |
| WOSCitedRecordID | wos001246725200002&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 customDbUrl: eissn: 1872-9681 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016928 issn: 1568-4946 databaseCode: AIEXJ dateStart: 20010601 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9swDBa2tIdd1q3t0HYP6LCbocAv2dIxGFLsERQ7pEB68iRH6lw4TpE4Q39-aVlyHu2K9bCDBUOWaMP8TNIURSL0WVKtUkAKAfBIEseKEqZUTPxQUz_MBbQmZf4ovbhgkwn_aVfwl6acQFpV7O6O3_5XVkMfMLvZOvsMdndEoQPOgenQAtuh_SfGD7wfZKZAA5Gxi5QcOf9Ho7Om3hzExMzuv_REeT1fFPXvmQk4bDKBl6UqvZmZqjz4-QVlZPesm9ozm-ass2GXIMxNdPqqdqqwCfIxkQJXhdzqWxngFOs1IeuxHqniwaBKrzbdEmHcBcV1kjRhJObWv-hEbZt43QpLELNpe_2BHG9dCjd9ARDtN-T768HbSbN3lFkXYuii126yhkbW0MhaGi_RXphSznpob_BtOPneLTol3JTi7Z7c7rFqwwF3n-RxO2bDNhm_Qa_tTwUetGB4i16o6hAduIId2MrvI_RrgHexgR02sMEG3sQG7rCBARvYYQNbbOA1NrDFxjG6PB-Ov3wltsQGySPfr0mShn4CVp6OpkmseSJ0SJWQOuI6CCSDbzedCsYp11EgRArmcSSE0kyyPA5zlkfvUK-aV-oEYc0ZAwJUxZICKS3B9FOMSj-RwRSOUxS4F5blNv98UwalzP7OqlPkdXNu2-wrT46mjg-ZtR9buzADWD0x7-xZd3mPXq3x_gH16sVKfUT7-Z-6WC4-WUzdA4fXkcw |
| 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=A+K-means-Teaching+Learning+based+optimization+algorithm+for+parallel+machine+scheduling+problem&rft.jtitle=Applied+soft+computing&rft.au=Li%2C+Yibing&rft.au=Liu%2C+Jie&rft.au=Wang%2C+Lei&rft.au=Liu%2C+Jinfu&rft.date=2024-08-01&rft.issn=1568-4946&rft.volume=161&rft.spage=111746&rft_id=info:doi/10.1016%2Fj.asoc.2024.111746&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_asoc_2024_111746 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1568-4946&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1568-4946&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1568-4946&client=summon |