A hybrid multi-objective approach for real-time flexible production scheduling and rescheduling under dynamic environment in Industry 4.0 context
•Incorporation of CP into genetic algorithm.•Consideration of rescheduling upon disruptions.•Detailed discussion on parameter adaption. With the advent of industry-4.0 era, industrial production are evolving towards high flexibility, diversity, customisation, and dynamism. We address a realistic sce...
Uložené v:
| Vydané v: | Computers & operations research Ročník 132; s. 105267 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
Elsevier Ltd
01.08.2021
Pergamon Press Inc |
| Predmet: | |
| ISSN: | 0305-0548, 0305-0548 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | •Incorporation of CP into genetic algorithm.•Consideration of rescheduling upon disruptions.•Detailed discussion on parameter adaption.
With the advent of industry-4.0 era, industrial production are evolving towards high flexibility, diversity, customisation, and dynamism. We address a realistic scenario of a smart manufacturing system, which concerns the production scheduling of complex multi-level products under a dynamic flexible job shop environment with shop floor disruptions incorporated. The products are assembled from multiple basic parts, whose fabrication processes are highly flexible, involving alternative process plans, alternative machines and alternative processing sequences of operations. We aim at providing Pareto solutions, with consideration of three typical optimisation objectives, including makespan, maximum machine workload, and total tardiness. A hybrid MPGA-CP approach is designed for the problem. To the best our knowledge, this is the first attempt to embed an exact optimisation technique into a meta-heuristic algorithm in the domain of production scheduling. Compared with other alternative approaches, its efficiency and performance are proven to be outstanding in solving medium-to-large scale problems, covering the largest proportion of Pareto solutions among all tested approaches. Furthermore, we constructed a simulation model of a real-time production scheduling control system, in which our approach is embedded as the kernel algorithm, to study the impacts of some uncertainties that are concerned in practice. Based on the results of simulation experiments and sensitivity analysis, meaningful managerial insights have been provided. |
|---|---|
| AbstractList | With the advent of industry-4.0 era, industrial production are evolving towards high flexibility, diversity, customisation, and dynamism. We address a realistic scenario of a smart manufacturing system, which concerns the production scheduling of complex multi-level products under a dynamic flexible job shop environment with shop floor disruptions incorporated. The products are assembled from multiple basic parts, whose fabrication processes are highly flexible, involving alternative process plans, alternative machines and alternative processing sequences of operations. We aim at providing Pareto solutions, with consideration of three typical optimisation objectives, including makespan, maximum machine workload, and total tardiness. A hybrid MPGA-CP approach is designed for the problem. To the best our knowledge, this is the first attempt to embed an exact optimisation technique into a meta-heuristic algorithm in the domain of production scheduling. Compared with other alternative approaches, its efficiency and performance are proven to be outstanding in solving medium-to-large scale problems, covering the largest proportion of Pareto solutions among all tested approaches. Furthermore, we constructed a simulation model of a real-time production scheduling control system, in which our approach is embedded as the kernel algorithm, to study the impacts of some uncertainties that are concerned in practice. Based on the results of simulation experiments and sensitivity analysis, meaningful managerial insights have been provided. •Incorporation of CP into genetic algorithm.•Consideration of rescheduling upon disruptions.•Detailed discussion on parameter adaption. With the advent of industry-4.0 era, industrial production are evolving towards high flexibility, diversity, customisation, and dynamism. We address a realistic scenario of a smart manufacturing system, which concerns the production scheduling of complex multi-level products under a dynamic flexible job shop environment with shop floor disruptions incorporated. The products are assembled from multiple basic parts, whose fabrication processes are highly flexible, involving alternative process plans, alternative machines and alternative processing sequences of operations. We aim at providing Pareto solutions, with consideration of three typical optimisation objectives, including makespan, maximum machine workload, and total tardiness. A hybrid MPGA-CP approach is designed for the problem. To the best our knowledge, this is the first attempt to embed an exact optimisation technique into a meta-heuristic algorithm in the domain of production scheduling. Compared with other alternative approaches, its efficiency and performance are proven to be outstanding in solving medium-to-large scale problems, covering the largest proportion of Pareto solutions among all tested approaches. Furthermore, we constructed a simulation model of a real-time production scheduling control system, in which our approach is embedded as the kernel algorithm, to study the impacts of some uncertainties that are concerned in practice. Based on the results of simulation experiments and sensitivity analysis, meaningful managerial insights have been provided. |
| ArticleNumber | 105267 |
| Author | Zhang, Sicheng Li, Xiang Tang, Fangcheng Liu, Jiaming Zhang, Bowen |
| Author_xml | – sequence: 1 givenname: Sicheng surname: Zhang fullname: Zhang, Sicheng – sequence: 2 givenname: Fangcheng surname: Tang fullname: Tang, Fangcheng email: tangfc@mail.buct.edu.cn – sequence: 3 givenname: Xiang surname: Li fullname: Li, Xiang – sequence: 4 givenname: Jiaming surname: Liu fullname: Liu, Jiaming – sequence: 5 givenname: Bowen surname: Zhang fullname: Zhang, Bowen |
| BookMark | eNp9kc1OxCAUhYnRRB19AHckrjsCLdNpXBnjX2LiRteEXm4dmhZGoBPnMXxjmYwL40I2Fy7nu-QcTsmh8w4JueBszhlfXPVz8GEumOD5LMWiPiAnrGSyYLJaHv7aH5PTGHuWVy34Cfm6oattG6yh4zQkW_i2R0h2g1Sv18FrWNHOBxpQD0WyI9JuwE_bDkjzrZmy1DsaYYVmGqx7p9qZLP7VmJzBQM3W6dECRbexwbsRXaLW0SdnppjCllZzRsG7hJ_pjBx1eoh4_lNn5O3-7vX2sXh-eXi6vXkuoJR1Kkpo0DTlUjC2wI4L0BUroawNcOg4tnVTC2kaDlpz0XQtBxAtIlYSO0TdljNyuZ-bjXxMGJPq_RRcflIJWUkhJePLrKr3Kgg-xoCdApv0znUK2g6KM7XLX_Uq5692-at9_pnkf8h1sKMO23-Z6z2D2fjGYlARLDpAY0P-FmW8_Yf-Bly-o8o |
| CitedBy_id | crossref_primary_10_1016_j_jmsy_2023_07_011 crossref_primary_10_3390_math10142395 crossref_primary_10_1371_journal_pone_0290789 crossref_primary_10_1016_j_eswa_2022_119375 crossref_primary_10_1016_j_asoc_2025_113355 crossref_primary_10_1109_TETCI_2022_3174915 crossref_primary_10_1016_j_cor_2022_106134 crossref_primary_10_3390_app11219821 crossref_primary_10_1016_j_cor_2023_106433 crossref_primary_10_1080_09537287_2023_2248947 crossref_primary_10_3390_e24010051 crossref_primary_10_1016_j_swevo_2024_101750 crossref_primary_10_1016_j_cie_2023_109550 crossref_primary_10_2478_amns_2024_0525 crossref_primary_10_1109_ACCESS_2022_3191426 crossref_primary_10_1109_LRA_2022_3184795 crossref_primary_10_3390_math13162605 crossref_primary_10_1108_IJSBI_05_2023_0027 crossref_primary_10_1016_j_cie_2022_108786 crossref_primary_10_1016_j_engappai_2023_106750 crossref_primary_10_1007_s10845_024_02484_2 crossref_primary_10_1016_j_jmsy_2022_08_008 crossref_primary_10_1016_j_tsep_2024_102753 crossref_primary_10_1109_ACCESS_2023_3311362 crossref_primary_10_1016_j_cor_2023_106266 crossref_primary_10_3390_su151813785 crossref_primary_10_1038_s41598_023_28630_z crossref_primary_10_1080_0305215X_2024_2335284 crossref_primary_10_3390_math12203176 crossref_primary_10_1109_ACCESS_2023_3261883 |
| Cites_doi | 10.1016/j.cie.2009.09.003 10.1016/j.cie.2016.03.011 10.1016/j.cor.2011.04.006 10.1016/j.cie.2008.11.017 10.1016/j.ijpe.2017.12.003 10.1109/TEM.2017.2785774 10.1007/s10845-017-1350-2 10.1016/j.ijpe.2018.10.010 10.1007/s00170-011-3409-0 10.1016/j.cor.2014.02.005 10.1016/j.cor.2011.10.007 10.1016/j.cor.2017.07.012 10.1016/j.cie.2017.01.006 10.1016/j.cor.2020.105031 10.1016/j.cor.2012.02.024 10.1080/00207543.2012.720393 10.1016/j.cor.2020.104951 10.1080/00207543.2016.1267414 10.1080/00207543.2015.1086037 10.1016/j.cie.2020.106863 10.1016/j.rcim.2007.01.001 10.1016/j.ejor.2018.04.033 10.1007/s10845-014-1023-3 10.1111/itor.12199 10.1002/(SICI)1099-1360(199801)7:1<34::AID-MCDA161>3.0.CO;2-6 10.1186/2251-712X-8-26 10.1016/j.cor.2016.04.006 10.1016/j.ejor.2019.11.016 10.1016/S0305-0548(02)00063-1 10.1016/j.ejor.2016.07.030 10.1002/nav.21901 10.1080/00207543.2018.1504248 10.1016/j.sbspro.2015.03.234 10.1080/00207543.2013.848487 10.1016/j.cor.2009.06.008 10.1016/j.cie.2009.04.002 10.1080/00207540903524490 10.1016/j.ejor.2015.01.032 10.1016/j.cor.2020.105053 10.1016/j.cie.2020.106605 10.1016/j.eswa.2016.08.019 10.1016/j.ijpe.2017.01.014 10.1080/0951192X.2013.834468 |
| ContentType | Journal Article |
| Copyright | 2021 Elsevier Ltd Copyright Pergamon Press Inc. Aug 2021 |
| Copyright_xml | – notice: 2021 Elsevier Ltd – notice: Copyright Pergamon Press Inc. Aug 2021 |
| DBID | AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1016/j.cor.2021.105267 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Computer and Information Systems Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science Business |
| EISSN | 0305-0548 |
| ExternalDocumentID | 10_1016_j_cor_2021_105267 S0305054821000599 |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 186 1B1 1OL 1RT 1~. 1~5 29F 4.4 457 4G. 5GY 5VS 6J9 7-5 71M 8P~ 9JN 9JO AAAKF AAAKG AABNK AACTN AAEDT AAEDW AAFJI AAIKJ AAKOC AALRI AAOAW AAQXK AARIN AAXKI AAXUO AAYFN AAYOK ABAOU ABBOA ABDPE ABEFU ABFNM ABFRF ABJNI ABMAC ABMMH ABUCO ABWVN ABXDB ACDAQ ACGFO ACGFS ACNCT ACNNM ACRLP ACRPL ACZNC ADBBV ADEZE ADGUI ADJOM ADMUD ADNMO AEBSH AEFWE AEHXG AEIPS AEKER AENEX AFFNX AFJKZ AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHZHX AI. AIALX AIEXJ AIGVJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOMHK AOUOD APLSM ARUGR ASPBG AVARZ AVWKF AXJTR AZFZN BKOJK BKOMP BLXMC CS3 DU5 EBS EFJIC EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX HVGLF HZ~ H~9 IHE J1W KOM LY1 M41 MHUIS MO0 MS~ O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ PRBVW Q38 R2- RIG ROL RPZ RXW SDF SDG SDP SDS SES SEW SPC SPCBC SSB SSD SSO SSV SSW SSZ T5K TAE TN5 U5U UAO UPT VH1 WUQ XPP ZMT ~02 ~G- 9DU AATTM AAYWO AAYXX ACLOT ACVFH ADCNI AEUPX AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP CITATION EFKBS EFLBG ~HD 7SC 8FD AGCQF JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c357t-3c9ed9382006ef12ca403c37dc1cf1eb79725d91caa129fb1cc2beee45efeeab3 |
| ISICitedReferencesCount | 37 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000694869900007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0305-0548 |
| IngestDate | Wed Aug 13 07:45:29 EDT 2025 Sat Nov 29 03:23:43 EST 2025 Tue Nov 18 21:16:41 EST 2025 Sat Jan 18 16:10:18 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Integrated processing planning and assembly scheduling Genetic algorithm Constraint programming Dynamic production scheduling Multi-objective optimisation |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c357t-3c9ed9382006ef12ca403c37dc1cf1eb79725d91caa129fb1cc2beee45efeeab3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2545255018 |
| PQPubID | 45870 |
| ParticipantIDs | proquest_journals_2545255018 crossref_citationtrail_10_1016_j_cor_2021_105267 crossref_primary_10_1016_j_cor_2021_105267 elsevier_sciencedirect_doi_10_1016_j_cor_2021_105267 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-08-01 |
| PublicationDateYYYYMMDD | 2021-08-01 |
| PublicationDate_xml | – month: 08 year: 2021 text: 2021-08-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Computers & operations research |
| PublicationYear | 2021 |
| Publisher | Elsevier Ltd Pergamon Press Inc |
| Publisher_xml | – name: Elsevier Ltd – name: Pergamon Press Inc |
| References | Li, Li, Sambandam, Sethi, Zhang (b0110) 2018; 206 Chan, Wong, Chan (b0015) 2009; 57 Xiong, Fan, Jiang, Li (b0185) 2017; 257 Zhang, Ding, Zou, Qin, Fu (b0215) 2019; 30 Komaki, Teymourian, Kayvanfar, Booyavi (b0075) 2017; 105 Zhang, Wong (b0205) 2018; 29 Defersha, Rooyani (b0030) 2020; 147 Maleki-Darounkolaei, Modiri, Tavakkoli-Moghaddam, Seyyedi (b0130) 2012; 8 Nourali, Imanipour, Shahriari (b0145) 2012; 6 Wong, Zhang, Wang, Zhang (b0175) 2012; 50 Caldeira, Gnanavelbabu, Vaidyanathan (b0005) 2020; 149 Xiong, Xing, Wang, Lei, Han (b0180) 2014; 47 Zhang, Wang (b0190) 2018; 65 Zhang, Li, Zhang, Wang (b0220) 2020; 283 Ku, Beck (b0080) 2016; 73 Framinan, Perez-Gonzalez (b0045) 2017; 88 Kim, Park, Ko (b0065) 2003; 30 Na, Park (b0135) 2014; 52 Li, Gao, Shao, Zhang, Wang (b0100) 2010; 37 Chan, Wong, Chan (b0010) 2008; 24 Li, Liu, Sethi, Xu (b0105) 2017; 186 Zhang, Wong (b0200) 2017; 55 Rossit, Tohmé, Frutos (b0165) 2019; 57 Feng, Li, Sethi (b0040) 2018; 196 Lu, Huang, Yang (b0120) 2011; 49 Leung, Wong, Mak, Fung (b0095) 2010; 59 Qiao, Lv (b0160) 2012; 58 Zhang, Manier, Manier (b0210) 2012; 39 Ding, Gu (b0035) 2020; 121 Chaudhry, Khan (b0020) 2016; 23 Ghaleb, Zolfagharinia, Taghipour (b0055) 2020; 123 Petrović, Vuković, Mitić, Miljković (b0155) 2016; 64 Kubiak, Feng, Li, Sethi, Sriskandarajah (b0085) 2020; 67 Paul, Sridharan, Radha Ramanan (b0150) 2015; 189 Wong, Chan, Chan (b0170) 2009; 57 Zhang, Wong (b0195) 2015; 244 Lv, Qiao (b0125) 2014; 27 Czyzzak, Jaszkiewicz (b0025) 1998; 7 Kundakcı, Kulak (b0090) 2016; 96 Nourali, Imanipour (b0140) 2014; 14 Framinan, Perez-Gonzalez, Fernandez-Viagas (b0050) 2019; 273 Lin, Li, Wei, Wu (b0115) 2020; 124 Zhong, Xu, Chen, Huang (b0225) 2017; 55 Gromicho, van Hoorn, Saldanha-da-Gama, Timmer (b0060) 2012; 39 Kis, Kovács (b0070) 2012; 39 Kundakcı (10.1016/j.cor.2021.105267_b0090) 2016; 96 Zhang (10.1016/j.cor.2021.105267_b0195) 2015; 244 Chan (10.1016/j.cor.2021.105267_b0010) 2008; 24 Zhong (10.1016/j.cor.2021.105267_b0225) 2017; 55 Caldeira (10.1016/j.cor.2021.105267_b0005) 2020; 149 Gromicho (10.1016/j.cor.2021.105267_b0060) 2012; 39 Framinan (10.1016/j.cor.2021.105267_b0050) 2019; 273 Xiong (10.1016/j.cor.2021.105267_b0180) 2014; 47 Komaki (10.1016/j.cor.2021.105267_b0075) 2017; 105 Zhang (10.1016/j.cor.2021.105267_b0220) 2020; 283 Feng (10.1016/j.cor.2021.105267_b0040) 2018; 196 Kis (10.1016/j.cor.2021.105267_b0070) 2012; 39 Nourali (10.1016/j.cor.2021.105267_b0145) 2012; 6 Wong (10.1016/j.cor.2021.105267_b0170) 2009; 57 Zhang (10.1016/j.cor.2021.105267_b0215) 2019; 30 Xiong (10.1016/j.cor.2021.105267_b0185) 2017; 257 Chan (10.1016/j.cor.2021.105267_b0015) 2009; 57 Defersha (10.1016/j.cor.2021.105267_b0030) 2020; 147 Lin (10.1016/j.cor.2021.105267_b0115) 2020; 124 Petrović (10.1016/j.cor.2021.105267_b0155) 2016; 64 Zhang (10.1016/j.cor.2021.105267_b0205) 2018; 29 Leung (10.1016/j.cor.2021.105267_b0095) 2010; 59 Zhang (10.1016/j.cor.2021.105267_b0190) 2018; 65 Maleki-Darounkolaei (10.1016/j.cor.2021.105267_b0130) 2012; 8 Ghaleb (10.1016/j.cor.2021.105267_b0055) 2020; 123 Li (10.1016/j.cor.2021.105267_b0110) 2018; 206 Rossit (10.1016/j.cor.2021.105267_b0165) 2019; 57 Framinan (10.1016/j.cor.2021.105267_b0045) 2017; 88 Ku (10.1016/j.cor.2021.105267_b0080) 2016; 73 Lu (10.1016/j.cor.2021.105267_b0120) 2011; 49 Nourali (10.1016/j.cor.2021.105267_b0140) 2014; 14 Czyzzak (10.1016/j.cor.2021.105267_b0025) 1998; 7 Li (10.1016/j.cor.2021.105267_b0100) 2010; 37 Paul (10.1016/j.cor.2021.105267_b0150) 2015; 189 Ding (10.1016/j.cor.2021.105267_b0035) 2020; 121 Kubiak (10.1016/j.cor.2021.105267_b0085) 2020; 67 Qiao (10.1016/j.cor.2021.105267_b0160) 2012; 58 Zhang (10.1016/j.cor.2021.105267_b0200) 2017; 55 Zhang (10.1016/j.cor.2021.105267_b0210) 2012; 39 Lv (10.1016/j.cor.2021.105267_b0125) 2014; 27 Chaudhry (10.1016/j.cor.2021.105267_b0020) 2016; 23 Li (10.1016/j.cor.2021.105267_b0105) 2017; 186 Na (10.1016/j.cor.2021.105267_b0135) 2014; 52 Wong (10.1016/j.cor.2021.105267_b0175) 2012; 50 Kim (10.1016/j.cor.2021.105267_b0065) 2003; 30 |
| References_xml | – volume: 64 start-page: 569 year: 2016 end-page: 588 ident: b0155 article-title: Integration of process planning and scheduling using chaotic particle swarm optimization algorithm publication-title: Expert Systems with Applications – volume: 39 start-page: 1713 year: 2012 end-page: 1723 ident: b0210 article-title: A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times publication-title: Computers & Operations Research – volume: 39 start-page: 320 year: 2012 end-page: 327 ident: b0070 article-title: A cutting plane approach for integrated planning and scheduling publication-title: Computers & Operations Research – volume: 196 start-page: 269 year: 2018 end-page: 283 ident: b0040 article-title: A three-layer chromosome genetic algorithm for multi-cell scheduling with flexible routes and machine sharing publication-title: International Journal of Production Economics – volume: 55 start-page: 3173 year: 2017 end-page: 3196 ident: b0200 article-title: Flexible job-shop scheduling/rescheduling in dynamic environment: A hybrid MAS/ACO approach publication-title: International Journal of Production Research – volume: 147 year: 2020 ident: b0030 article-title: An efficient two-stage genetic algorithm for a flexible job-shop scheduling problem with sequence dependent attached/detached setup, machine release date and lag-time publication-title: Computers & Industrial Engineering – volume: 121 year: 2020 ident: b0035 article-title: Improved particle swarm optimization algorithm based novel encoding and decoding schemes for flexible job shop scheduling problem publication-title: Computers & Operations Research – volume: 6 start-page: 2117 year: 2012 end-page: 2132 ident: b0145 article-title: A mathematical model for integrated process planning and scheduling in flexible assembly job shop environment with sequence dependent setup times publication-title: International Journal of Mathematical Analysis – volume: 57 start-page: 3802 year: 2019 end-page: 3813 ident: b0165 article-title: Industry 4.0: Smart Scheduling publication-title: International Journal of Production Research – volume: 30 start-page: 1809 year: 2019 end-page: 1830 ident: b0215 article-title: Review of job shop scheduling research and its new perspectives under Industry 4.0 publication-title: Journal of Intelligent Manufacturing – volume: 58 start-page: 727 year: 2012 end-page: 740 ident: b0160 article-title: An improved genetic algorithm for integrated process planning and scheduling publication-title: The International Journal of Advanced Manufacturing Technology – volume: 244 start-page: 434 year: 2015 end-page: 444 ident: b0195 article-title: An object-coding genetic algorithm for integrated process planning and scheduling publication-title: European Journal of Operational Research – volume: 7 start-page: 34 year: 1998 end-page: 47 ident: b0025 article-title: Pareto simulated annealing—a metaheuristic technique for multiple objective combinatorial optimization publication-title: Journal of Multi-Criteria Decision Analysis – volume: 57 start-page: 983 year: 2009 end-page: 995 ident: b0170 article-title: A resource-constrained assembly job shop scheduling problem with lot streaming technique publication-title: Computers & Industrial Engineering – volume: 283 start-page: 441 year: 2020 end-page: 460 ident: b0220 article-title: Multi-objective optimisation in flexible assembly job shop scheduling using a distributed ant colony system publication-title: European Journal of Operational Research – volume: 14 year: 2014 ident: b0140 article-title: A particle swarm optimization-based algorithm for flexible assembly job shop scheduling problem with sequence dependent setup times publication-title: Scientia Iranica – volume: 30 start-page: 1151 year: 2003 end-page: 1171 ident: b0065 article-title: A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling publication-title: Computers & Operations Research – volume: 39 start-page: 2968 year: 2012 end-page: 2977 ident: b0060 article-title: Solving the job-shop scheduling problem optimally by dynamic programming publication-title: Computers & Operations Research – volume: 105 start-page: 158 year: 2017 end-page: 173 ident: b0075 article-title: Improved discrete cuckoo optimization algorithm for the three-stage assembly flowshop scheduling problem publication-title: Computers & Industrial Engineering – volume: 29 start-page: 585 year: 2018 end-page: 601 ident: b0205 article-title: Integrated process planning and scheduling: An enhanced ant colony optimization heuristic with parameter tuning publication-title: Journal of Intelligent Manufacturing – volume: 189 start-page: 376 year: 2015 end-page: 384 ident: b0150 article-title: An investigation of order review/release policies and dispatching rules for assembly job shops with multi objective criteria publication-title: Procedia – Social and Behavioral Sciences – volume: 73 start-page: 165 year: 2016 end-page: 173 ident: b0080 article-title: Mixed Integer Programming models for job shop scheduling: A computational analysis publication-title: Computers & Operations Research – volume: 52 start-page: 3877 year: 2014 end-page: 3887 ident: b0135 article-title: Multi-level job scheduling in a flexible job shop environment publication-title: International Journal of Production Research – volume: 206 start-page: 250 year: 2018 end-page: 260 ident: b0110 article-title: Flow shop scheduling with jobs arriving at different times publication-title: International Journal of Production Economics – volume: 23 start-page: 551 year: 2016 end-page: 591 ident: b0020 article-title: A research survey: Review of flexible job shop scheduling techniques publication-title: International Transactions in Operational Research – volume: 123 year: 2020 ident: b0055 article-title: Real-time production scheduling in the Industry-4.0 context: Addressing uncertainties in job arrivals and machine breakdowns publication-title: Computers & Operations Research – volume: 49 start-page: 647 year: 2011 end-page: 669 ident: b0120 article-title: Integrating order review/release and dispatching rules for assembly job shop scheduling using a simulation approach publication-title: International Journal of Production Research – volume: 47 start-page: 92 year: 2014 end-page: 105 ident: b0180 article-title: Minimizing the total completion time in a distributed two stage assembly system with setup times publication-title: Computers & Operations Research – volume: 8 year: 2012 ident: b0130 article-title: A three-stage assembly flow shop scheduling problem with blocking and sequence-dependent set up times publication-title: Journal of Industrial Engineering International – volume: 186 start-page: 1 year: 2017 end-page: 7 ident: b0105 article-title: Parallel-machine scheduling with machine-dependent maintenance periodic recycles publication-title: International Journal of Production Economics – volume: 65 start-page: 487 year: 2018 end-page: 504 ident: b0190 article-title: Flexible assembly job-shop scheduling with sequence-dependent setup times and part sharing in a dynamic environment: constraint programming model, mixed-integer programming model, and dispatching rules publication-title: IEEE Transactions on Engineering Management – volume: 149 year: 2020 ident: b0005 article-title: An effective backtracking search algorithm for multi-objective flexible job shop scheduling considering new job arrivals and energy consumption publication-title: Computers & Industrial Engineering – volume: 57 start-page: 641 year: 2009 end-page: 651 ident: b0015 article-title: An evolutionary algorithm for assembly job shop with part sharing publication-title: Computers & Industrial Engineering – volume: 273 start-page: 401 year: 2019 end-page: 417 ident: b0050 article-title: Deterministic assembly scheduling problems: A review and classification of concurrent-type scheduling models and solution procedures publication-title: European Journal of Operational Research – volume: 59 start-page: 166 year: 2010 end-page: 180 ident: b0095 article-title: Integrated process planning and scheduling by an agent-based ant colony optimization publication-title: Computers & Industrial Engineering – volume: 37 start-page: 656 year: 2010 end-page: 667 ident: b0100 article-title: Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling publication-title: Computers & Operations Research – volume: 55 start-page: 2610 year: 2017 end-page: 2621 ident: b0225 article-title: Big data analytics for physical internet-based intelligent manufacturing shop floors publication-title: International Journal of Production Research – volume: 257 start-page: 13 year: 2017 end-page: 24 ident: b0185 article-title: A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints publication-title: European Journal of Operational Research – volume: 88 start-page: 237 year: 2017 end-page: 246 ident: b0045 article-title: The 2-stage assembly flowshop scheduling problem with total completion time: Efficient constructive heuristic and metaheuristic publication-title: Computers & Operations Research – volume: 24 start-page: 321 year: 2008 end-page: 331 ident: b0010 article-title: Lot streaming for product assembly in job shop environment publication-title: Robotics and Computer-Integrated Manufacturing – volume: 67 start-page: 272 year: 2020 end-page: 288 ident: b0085 article-title: Efficient algorithms for flexible job shop scheduling with parallel machines publication-title: Naval Research Logistics (NRL) – volume: 27 start-page: 638 year: 2014 end-page: 655 ident: b0125 article-title: Process planning and scheduling integration with optimal rescheduling strategies publication-title: International Journal of Computer Integrated Manufacturing – volume: 96 start-page: 31 year: 2016 end-page: 51 ident: b0090 article-title: Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem publication-title: Computers & Industrial Engineering – volume: 124 year: 2020 ident: b0115 article-title: Integration of process planning and scheduling for distributed flexible job shops publication-title: Computers & Operations Research – volume: 50 start-page: 6188 year: 2012 end-page: 6201 ident: b0175 article-title: Integrated process planning and scheduling – multi-agent system with two-stage ant colony optimisation algorithm publication-title: International Journal of Production Research – volume: 59 start-page: 166 year: 2010 ident: 10.1016/j.cor.2021.105267_b0095 article-title: Integrated process planning and scheduling by an agent-based ant colony optimization publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2009.09.003 – volume: 96 start-page: 31 year: 2016 ident: 10.1016/j.cor.2021.105267_b0090 article-title: Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2016.03.011 – volume: 39 start-page: 320 year: 2012 ident: 10.1016/j.cor.2021.105267_b0070 article-title: A cutting plane approach for integrated planning and scheduling publication-title: Computers & Operations Research doi: 10.1016/j.cor.2011.04.006 – volume: 57 start-page: 641 year: 2009 ident: 10.1016/j.cor.2021.105267_b0015 article-title: An evolutionary algorithm for assembly job shop with part sharing publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2008.11.017 – volume: 196 start-page: 269 year: 2018 ident: 10.1016/j.cor.2021.105267_b0040 article-title: A three-layer chromosome genetic algorithm for multi-cell scheduling with flexible routes and machine sharing publication-title: International Journal of Production Economics doi: 10.1016/j.ijpe.2017.12.003 – volume: 65 start-page: 487 year: 2018 ident: 10.1016/j.cor.2021.105267_b0190 article-title: Flexible assembly job-shop scheduling with sequence-dependent setup times and part sharing in a dynamic environment: constraint programming model, mixed-integer programming model, and dispatching rules publication-title: IEEE Transactions on Engineering Management doi: 10.1109/TEM.2017.2785774 – volume: 30 start-page: 1809 year: 2019 ident: 10.1016/j.cor.2021.105267_b0215 article-title: Review of job shop scheduling research and its new perspectives under Industry 4.0 publication-title: Journal of Intelligent Manufacturing doi: 10.1007/s10845-017-1350-2 – volume: 206 start-page: 250 year: 2018 ident: 10.1016/j.cor.2021.105267_b0110 article-title: Flow shop scheduling with jobs arriving at different times publication-title: International Journal of Production Economics doi: 10.1016/j.ijpe.2018.10.010 – volume: 58 start-page: 727 year: 2012 ident: 10.1016/j.cor.2021.105267_b0160 article-title: An improved genetic algorithm for integrated process planning and scheduling publication-title: The International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-011-3409-0 – volume: 47 start-page: 92 year: 2014 ident: 10.1016/j.cor.2021.105267_b0180 article-title: Minimizing the total completion time in a distributed two stage assembly system with setup times publication-title: Computers & Operations Research doi: 10.1016/j.cor.2014.02.005 – volume: 39 start-page: 1713 year: 2012 ident: 10.1016/j.cor.2021.105267_b0210 article-title: A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times publication-title: Computers & Operations Research doi: 10.1016/j.cor.2011.10.007 – volume: 88 start-page: 237 year: 2017 ident: 10.1016/j.cor.2021.105267_b0045 article-title: The 2-stage assembly flowshop scheduling problem with total completion time: Efficient constructive heuristic and metaheuristic publication-title: Computers & Operations Research doi: 10.1016/j.cor.2017.07.012 – volume: 105 start-page: 158 year: 2017 ident: 10.1016/j.cor.2021.105267_b0075 article-title: Improved discrete cuckoo optimization algorithm for the three-stage assembly flowshop scheduling problem publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2017.01.006 – volume: 6 start-page: 2117 year: 2012 ident: 10.1016/j.cor.2021.105267_b0145 article-title: A mathematical model for integrated process planning and scheduling in flexible assembly job shop environment with sequence dependent setup times publication-title: International Journal of Mathematical Analysis – volume: 123 year: 2020 ident: 10.1016/j.cor.2021.105267_b0055 article-title: Real-time production scheduling in the Industry-4.0 context: Addressing uncertainties in job arrivals and machine breakdowns publication-title: Computers & Operations Research doi: 10.1016/j.cor.2020.105031 – volume: 39 start-page: 2968 year: 2012 ident: 10.1016/j.cor.2021.105267_b0060 article-title: Solving the job-shop scheduling problem optimally by dynamic programming publication-title: Computers & Operations Research doi: 10.1016/j.cor.2012.02.024 – volume: 50 start-page: 6188 year: 2012 ident: 10.1016/j.cor.2021.105267_b0175 article-title: Integrated process planning and scheduling – multi-agent system with two-stage ant colony optimisation algorithm publication-title: International Journal of Production Research doi: 10.1080/00207543.2012.720393 – volume: 121 year: 2020 ident: 10.1016/j.cor.2021.105267_b0035 article-title: Improved particle swarm optimization algorithm based novel encoding and decoding schemes for flexible job shop scheduling problem publication-title: Computers & Operations Research doi: 10.1016/j.cor.2020.104951 – volume: 14 year: 2014 ident: 10.1016/j.cor.2021.105267_b0140 article-title: A particle swarm optimization-based algorithm for flexible assembly job shop scheduling problem with sequence dependent setup times publication-title: Scientia Iranica – volume: 55 start-page: 3173 year: 2017 ident: 10.1016/j.cor.2021.105267_b0200 article-title: Flexible job-shop scheduling/rescheduling in dynamic environment: A hybrid MAS/ACO approach publication-title: International Journal of Production Research doi: 10.1080/00207543.2016.1267414 – volume: 55 start-page: 2610 year: 2017 ident: 10.1016/j.cor.2021.105267_b0225 article-title: Big data analytics for physical internet-based intelligent manufacturing shop floors publication-title: International Journal of Production Research doi: 10.1080/00207543.2015.1086037 – volume: 149 year: 2020 ident: 10.1016/j.cor.2021.105267_b0005 article-title: An effective backtracking search algorithm for multi-objective flexible job shop scheduling considering new job arrivals and energy consumption publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2020.106863 – volume: 24 start-page: 321 year: 2008 ident: 10.1016/j.cor.2021.105267_b0010 article-title: Lot streaming for product assembly in job shop environment publication-title: Robotics and Computer-Integrated Manufacturing doi: 10.1016/j.rcim.2007.01.001 – volume: 273 start-page: 401 year: 2019 ident: 10.1016/j.cor.2021.105267_b0050 article-title: Deterministic assembly scheduling problems: A review and classification of concurrent-type scheduling models and solution procedures publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2018.04.033 – volume: 29 start-page: 585 year: 2018 ident: 10.1016/j.cor.2021.105267_b0205 article-title: Integrated process planning and scheduling: An enhanced ant colony optimization heuristic with parameter tuning publication-title: Journal of Intelligent Manufacturing doi: 10.1007/s10845-014-1023-3 – volume: 23 start-page: 551 year: 2016 ident: 10.1016/j.cor.2021.105267_b0020 article-title: A research survey: Review of flexible job shop scheduling techniques publication-title: International Transactions in Operational Research doi: 10.1111/itor.12199 – volume: 7 start-page: 34 year: 1998 ident: 10.1016/j.cor.2021.105267_b0025 article-title: Pareto simulated annealing—a metaheuristic technique for multiple objective combinatorial optimization publication-title: Journal of Multi-Criteria Decision Analysis doi: 10.1002/(SICI)1099-1360(199801)7:1<34::AID-MCDA161>3.0.CO;2-6 – volume: 8 year: 2012 ident: 10.1016/j.cor.2021.105267_b0130 article-title: A three-stage assembly flow shop scheduling problem with blocking and sequence-dependent set up times publication-title: Journal of Industrial Engineering International doi: 10.1186/2251-712X-8-26 – volume: 73 start-page: 165 year: 2016 ident: 10.1016/j.cor.2021.105267_b0080 article-title: Mixed Integer Programming models for job shop scheduling: A computational analysis publication-title: Computers & Operations Research doi: 10.1016/j.cor.2016.04.006 – volume: 283 start-page: 441 year: 2020 ident: 10.1016/j.cor.2021.105267_b0220 article-title: Multi-objective optimisation in flexible assembly job shop scheduling using a distributed ant colony system publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2019.11.016 – volume: 30 start-page: 1151 year: 2003 ident: 10.1016/j.cor.2021.105267_b0065 article-title: A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling publication-title: Computers & Operations Research doi: 10.1016/S0305-0548(02)00063-1 – volume: 257 start-page: 13 year: 2017 ident: 10.1016/j.cor.2021.105267_b0185 article-title: A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2016.07.030 – volume: 67 start-page: 272 year: 2020 ident: 10.1016/j.cor.2021.105267_b0085 article-title: Efficient algorithms for flexible job shop scheduling with parallel machines publication-title: Naval Research Logistics (NRL) doi: 10.1002/nav.21901 – volume: 57 start-page: 3802 year: 2019 ident: 10.1016/j.cor.2021.105267_b0165 article-title: Industry 4.0: Smart Scheduling publication-title: International Journal of Production Research doi: 10.1080/00207543.2018.1504248 – volume: 189 start-page: 376 year: 2015 ident: 10.1016/j.cor.2021.105267_b0150 article-title: An investigation of order review/release policies and dispatching rules for assembly job shops with multi objective criteria publication-title: Procedia – Social and Behavioral Sciences doi: 10.1016/j.sbspro.2015.03.234 – volume: 52 start-page: 3877 year: 2014 ident: 10.1016/j.cor.2021.105267_b0135 article-title: Multi-level job scheduling in a flexible job shop environment publication-title: International Journal of Production Research doi: 10.1080/00207543.2013.848487 – volume: 37 start-page: 656 year: 2010 ident: 10.1016/j.cor.2021.105267_b0100 article-title: Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling publication-title: Computers & Operations Research doi: 10.1016/j.cor.2009.06.008 – volume: 57 start-page: 983 year: 2009 ident: 10.1016/j.cor.2021.105267_b0170 article-title: A resource-constrained assembly job shop scheduling problem with lot streaming technique publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2009.04.002 – volume: 49 start-page: 647 year: 2011 ident: 10.1016/j.cor.2021.105267_b0120 article-title: Integrating order review/release and dispatching rules for assembly job shop scheduling using a simulation approach publication-title: International Journal of Production Research doi: 10.1080/00207540903524490 – volume: 244 start-page: 434 year: 2015 ident: 10.1016/j.cor.2021.105267_b0195 article-title: An object-coding genetic algorithm for integrated process planning and scheduling publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2015.01.032 – volume: 124 year: 2020 ident: 10.1016/j.cor.2021.105267_b0115 article-title: Integration of process planning and scheduling for distributed flexible job shops publication-title: Computers & Operations Research doi: 10.1016/j.cor.2020.105053 – volume: 147 year: 2020 ident: 10.1016/j.cor.2021.105267_b0030 article-title: An efficient two-stage genetic algorithm for a flexible job-shop scheduling problem with sequence dependent attached/detached setup, machine release date and lag-time publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2020.106605 – volume: 64 start-page: 569 year: 2016 ident: 10.1016/j.cor.2021.105267_b0155 article-title: Integration of process planning and scheduling using chaotic particle swarm optimization algorithm publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2016.08.019 – volume: 186 start-page: 1 year: 2017 ident: 10.1016/j.cor.2021.105267_b0105 article-title: Parallel-machine scheduling with machine-dependent maintenance periodic recycles publication-title: International Journal of Production Economics doi: 10.1016/j.ijpe.2017.01.014 – volume: 27 start-page: 638 year: 2014 ident: 10.1016/j.cor.2021.105267_b0125 article-title: Process planning and scheduling integration with optimal rescheduling strategies publication-title: International Journal of Computer Integrated Manufacturing doi: 10.1080/0951192X.2013.834468 |
| SSID | ssj0000721 |
| Score | 2.5126595 |
| Snippet | •Incorporation of CP into genetic algorithm.•Consideration of rescheduling upon disruptions.•Detailed discussion on parameter adaption.
With the advent of... With the advent of industry-4.0 era, industrial production are evolving towards high flexibility, diversity, customisation, and dynamism. We address a... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 105267 |
| SubjectTerms | Algorithms Constraint programming Dynamic production scheduling Genetic algorithm Heuristic methods Industry 4.0 Integrated processing planning and assembly scheduling Job shop scheduling Job shops Multi-objective optimisation Multiple objective analysis Operations research Optimization Production scheduling Real time Scheduling Sensitivity analysis |
| Title | A hybrid multi-objective approach for real-time flexible production scheduling and rescheduling under dynamic environment in Industry 4.0 context |
| URI | https://dx.doi.org/10.1016/j.cor.2021.105267 https://www.proquest.com/docview/2545255018 |
| Volume | 132 |
| WOSCitedRecordID | wos000694869900007&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: 0305-0548 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000721 issn: 0305-0548 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Nb9NAEF2FFiE48BGKKBQ0B05ErmI7znqPEWoFqKo4FJSbZe-uRaPIiZK0an8Gf4VfyMx-2Y1oRQ9cnGhir1aal53Z8by3jH1QokwrjMuRUPk4GsnxMKrqWkR5ppXEiKqEEZ7_ccJPT_PpVHzr9X57LszlnDdNfnUllv_V1WhDZxN19h7uDoOiAb-j0_GKbsfrPzl-Mvh5TTQs2ysYLaqZXdOCfLjpLMRccR7RwfKDmiQxiT-1tOKvhAfc8mIImnsCI27JWwOxzlYDZU-y7xLlqHTiTgK5HowOh6YNHtf-bv7rD5FYG8gtlnrlWvGc6FAoTreFbOpVdeHVVBis-Rg_b_xwYtoSpoj2junCgJS4687q6htJHLrrAq-L2gszq8cZ1mxXFLWrbkyiNfyvAcHWJmboTxJ_TeLD9t6b4ttbQTG0KvouuFmBQxQ0RGGHeMB2E54JDAa7ky9H069t_OeG7Rfm7d-lm67CrXnclg1t5QUm2Tl7zp66XQpMLLpesJ5u-uyRJ0n02TPvR3Cxoc-edJQtX7JfE7AohC0UgkchIAohoBA8CqFFIbSgA0QhdFEIBoXgUAgdFMJ5Ax6FgCgEh8I99v346OzT58id_hHJNOObKJVCK5HmVPPSdZzIcjRMZcqVjGUd64oLnmRKxLIsMWetq1jKpNJajzJda11W6Su20ywa_ZrBWMtUal6NiQWueFnWqYq1zrnKMHxxuc-G3guFdNL4dELLvLjV-_vsY3hkaXVh7rp55F1buMTWJqwFwvSuxw48DAq3wKyLJKNOBJLhfHOfKbxlj9s_1wHb2awu9Dv2UF5uzter9w7CfwCKedTS |
| 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+hybrid+multi-objective+approach+for+real-time+flexible+production+scheduling+and+rescheduling+under+dynamic+environment+in+Industry+4.0+context&rft.jtitle=Computers+%26+operations+research&rft.au=Zhang%2C+Sicheng&rft.au=Tang%2C+Fangcheng&rft.au=Li%2C+Xiang&rft.au=Liu%2C+Jiaming&rft.date=2021-08-01&rft.issn=0305-0548&rft.volume=132&rft.spage=105267&rft_id=info:doi/10.1016%2Fj.cor.2021.105267&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_cor_2021_105267 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0305-0548&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0305-0548&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0305-0548&client=summon |