Modeling an Uncertain Productivity Learning Process Using an Interval Fuzzy Methodology
Existing methods for forecasting the productivity of a factory are subject to a major drawback—the lower and upper bounds of productivity are usually determined by a few extreme cases, which unacceptably widens the productivity range. To address this drawback, an interval fuzzy number (IFN)-based mi...
Uloženo v:
| Vydáno v: | Mathematics (Basel) Ročník 8; číslo 6; s. 998 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
MDPI AG
01.06.2020
|
| Témata: | |
| ISSN: | 2227-7390, 2227-7390 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Existing methods for forecasting the productivity of a factory are subject to a major drawback—the lower and upper bounds of productivity are usually determined by a few extreme cases, which unacceptably widens the productivity range. To address this drawback, an interval fuzzy number (IFN)-based mixed binary quadratic programming (MBQP)–ordered weighted average (OWA) approach is proposed in this study for modeling an uncertain productivity learning process. In the proposed methodology, the productivity range is divided into the inner and outer sections, which correspond to the lower and upper membership functions of an IFN-based fuzzy productivity forecast, respectively. In this manner, all actual values are included in the outer section, whereas most of the values are included within the inner section to fulfill different managerial purposes. According to the percentages of outlier cases, a suitable forecasting strategy can be selected. To derive the values of parameters in the IFN-based fuzzy productivity learning model, an MBQP model is proposed and optimized. Subsequently, according to the selected forecasting strategy, the OWA method is applied to defuzzify a fuzzy productivity forecast. The proposed methodology has been applied to the real case of a dynamic random access memory factory to evaluate its effectiveness. The experimental results indicate that the proposed methodology was superior to several existing methods, especially in terms of mean absolute error, mean absolute percentage error, and root mean square error in evaluating the forecasting accuracy. The forecasting precision achieved using the proposed methodology was also satisfactory. |
|---|---|
| AbstractList | Existing methods for forecasting the productivity of a factory are subject to a major drawback—the lower and upper bounds of productivity are usually determined by a few extreme cases, which unacceptably widens the productivity range. To address this drawback, an interval fuzzy number (IFN)-based mixed binary quadratic programming (MBQP)–ordered weighted average (OWA) approach is proposed in this study for modeling an uncertain productivity learning process. In the proposed methodology, the productivity range is divided into the inner and outer sections, which correspond to the lower and upper membership functions of an IFN-based fuzzy productivity forecast, respectively. In this manner, all actual values are included in the outer section, whereas most of the values are included within the inner section to fulfill different managerial purposes. According to the percentages of outlier cases, a suitable forecasting strategy can be selected. To derive the values of parameters in the IFN-based fuzzy productivity learning model, an MBQP model is proposed and optimized. Subsequently, according to the selected forecasting strategy, the OWA method is applied to defuzzify a fuzzy productivity forecast. The proposed methodology has been applied to the real case of a dynamic random access memory factory to evaluate its effectiveness. The experimental results indicate that the proposed methodology was superior to several existing methods, especially in terms of mean absolute error, mean absolute percentage error, and root mean square error in evaluating the forecasting accuracy. The forecasting precision achieved using the proposed methodology was also satisfactory. |
| Author | Chiu, Min-Chi Hsu, Keng-Wei Chen, Tin-Chih Toly |
| Author_xml | – sequence: 1 givenname: Min-Chi orcidid: 0000-0001-6938-2391 surname: Chiu fullname: Chiu, Min-Chi – sequence: 2 givenname: Tin-Chih Toly surname: Chen fullname: Chen, Tin-Chih Toly – sequence: 3 givenname: Keng-Wei surname: Hsu fullname: Hsu, Keng-Wei |
| BookMark | eNptkE9LAzEUxIMoWGtPfoG9y2o2_3ZzlGK10KIHi8flbTZpU7aJZNPC9tO7tUWK-C7zGGZ-h7lBl847jdBdhh8olfhxA3FVYIGlLC7QgBCSp3nvX57912jUtmvcn8xoweQAfc59rRvrlgm4ZOGUDhGsS96Dr7cq2p2NXTLTENwh0rtKt22yaE-FqYs67KBJJtv9vkvmOq587Ru_7G7RlYGm1aOTDtFi8vwxfk1nby_T8dMsVZSKmMqq4JRU2GRM1ZjWRZ7VmhFQQhPFgNccKkG04hWlGRcmzwyjBBNqiJCMcjpE0yO39rAuv4LdQOhKD7b8MXxYlhCiVY0uRSEZz7HJq0IwxTEQoikx0iicEy6hZ90fWSr4tg3a_PIyXB4mLs8m7tPZn7SyEaL1Lgawzb-db2lsgTQ |
| CitedBy_id | crossref_primary_10_1007_s40747_021_00416_8 crossref_primary_10_3390_math9101101 crossref_primary_10_3390_math9161892 crossref_primary_10_1007_s00500_023_09136_2 crossref_primary_10_3390_joitmc7010079 crossref_primary_10_3390_math9233009 crossref_primary_10_1177_21582440241241410 crossref_primary_10_3390_en14238107 crossref_primary_10_1007_s12652_020_02435_8 crossref_primary_10_1038_s41598_023_31518_7 crossref_primary_10_1109_TEM_2024_3374517 |
| Cites_doi | 10.1007/s40747-020-00130-x 10.1007/s12351-019-00489-x 10.1007/s00170-019-03691-5 10.1007/s00521-016-2270-3 10.1142/S0218488508005030 10.1016/j.eswa.2012.07.066 10.1016/0165-0114(94)90144-9 10.1007/s00521-018-03988-8 10.1016/j.knosys.2013.03.004 10.1016/j.neucom.2017.10.051 10.1007/s40747-018-0081-0 10.1007/s40747-018-0076-x 10.1016/j.asoc.2020.106455 10.1016/j.knosys.2012.11.007 10.1109/WEIT.2011.19 10.1016/j.fss.2007.04.013 10.3390/sym10020045 10.1007/s10588-017-9242-8 10.1016/j.mcm.2011.07.003 10.1109/TIM.2009.2036347 10.1016/j.asoc.2014.08.003 10.1007/s12652-019-01302-5 10.1007/s10588-017-9262-4 10.2991/ijcis.d.190712.001 10.1016/S0165-0114(96)00368-5 10.1007/s00521-017-3093-6 10.1016/0165-0114(89)90205-4 10.1007/s12351-019-00483-3 10.1007/978-3-642-23960-1_9 10.1016/0020-0255(75)90046-8 10.5539/ijbm.v6n7p164 10.1155/2013/234571 10.1016/0165-0114(88)90054-1 10.1016/j.cie.2013.07.014 10.1080/08956308.2002.11671501 10.1007/s00500-019-04394-5 10.1002/int.21866 10.1016/j.cie.2018.07.002 10.1016/j.fss.2004.10.022 10.1007/s00170-019-03998-3 10.5220/0007830700400050 10.3386/w24001 10.1002/int.22033 10.1016/j.promfg.2017.04.022 10.1007/s12652-017-0504-6 10.3390/su6129441 10.1007/s40747-019-0098-z 10.1109/TFUZZ.2013.2250290 10.1016/j.asoc.2009.03.006 10.1007/s10588-018-09287-w 10.1007/s00521-018-3492-3 10.1007/BF00159729 10.1007/978-3-642-17910-5 10.1007/s12652-018-0912-2 10.1016/j.promfg.2018.10.021 10.1007/s00170-013-5100-0 10.1177/0954405419896117 10.1002/jtr.2168 10.1126/science.1091277 10.1007/s12190-018-1193-9 10.1007/s10588-018-09284-z |
| ContentType | Journal Article |
| DBID | AAYXX CITATION DOA |
| DOI | 10.3390/math8060998 |
| DatabaseName | CrossRef DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Mathematics |
| EISSN | 2227-7390 |
| ExternalDocumentID | oai_doaj_org_article_6894570f7b864c50a22e32f9fc07259a 10_3390_math8060998 |
| GroupedDBID | -~X 5VS 85S 8FE 8FG AADQD AAFWJ AAYXX ABDBF ABJCF ABPPZ ABUWG ACIPV ACIWK ADBBV AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS AMVHM ARAPS AZQEC BCNDV BENPR BGLVJ BPHCQ CCPQU CITATION DWQXO GNUQQ GROUPED_DOAJ HCIFZ IAO K6V K7- KQ8 L6V M7S MODMG M~E OK1 PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC PTHSS RNS |
| ID | FETCH-LOGICAL-c336t-9b8532b0f14cd03d871de42ac6e2c4a5d5ab62ec5b33156f71f432023f2694353 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 11 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000554695400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2227-7390 |
| IngestDate | Tue Oct 14 19:05:08 EDT 2025 Tue Nov 18 20:57:10 EST 2025 Sat Nov 29 07:15:55 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c336t-9b8532b0f14cd03d871de42ac6e2c4a5d5ab62ec5b33156f71f432023f2694353 |
| ORCID | 0000-0001-6938-2391 |
| OpenAccessLink | https://doaj.org/article/6894570f7b864c50a22e32f9fc07259a |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_6894570f7b864c50a22e32f9fc07259a crossref_primary_10_3390_math8060998 crossref_citationtrail_10_3390_math8060998 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-06-01 |
| PublicationDateYYYYMMDD | 2020-06-01 |
| PublicationDate_xml | – month: 06 year: 2020 text: 2020-06-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Mathematics (Basel) |
| PublicationYear | 2020 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Lin (ref_50) 2019; 105 Zhang (ref_69) 2018; 20 Hu (ref_28) 2013; 43 Rahman (ref_64) 2019; 5 ref_13 ref_11 Xu (ref_27) 2019; 34 Lin (ref_47) 2020; 234 ref_54 ref_52 Zeng (ref_30) 2013; 40 Hashemian (ref_12) 2011; 60 Chen (ref_68) 2020; 94 Chen (ref_72) 2017; 32 Chen (ref_35) 2019; 131 Muhuri (ref_25) 2017; 26 Broumi (ref_23) 2019; 5 ref_61 Chen (ref_42) 2012; 8 Chen (ref_53) 2008; 16 Hidalgo (ref_56) 2018; 275 Asemi (ref_8) 2011; 6 Lee (ref_31) 2016; 24 ref_24 Atanassov (ref_59) 1989; 31 Emrouznejad (ref_18) 2011; 54 Chen (ref_21) 2019; 25 Zeng (ref_67) 2019; 12 Blancett (ref_63) 2002; 45 Mendel (ref_34) 2007; 2 ref_29 ref_26 Lin (ref_65) 2019; 5 Wei (ref_60) 2013; 46 (ref_46) 2018; 24 Guijun (ref_22) 1998; 98 Chen (ref_71) 2007; 158 Shamsuddin (ref_9) 2014; 1 Wang (ref_70) 2018; 17 Appelbaum (ref_14) 1991; 2 Hougaard (ref_17) 2005; 152 ref_39 Samanta (ref_40) 2019; 31 Chen (ref_57) 2009; 9 ref_38 (ref_5) 2001; 47 Chen (ref_6) 2013; 66 Akano (ref_58) 2017; 3 Zadeh (ref_16) 1975; 8 Chen (ref_55) 2013; 22 Wang (ref_19) 2013; 5 Tsai (ref_49) 2014; 6 Chen (ref_15) 2014; 24 Chen (ref_20) 2016; 87 ref_45 Javanmard (ref_33) 2019; 59 ref_44 Chen (ref_32) 2020; 11 ref_43 Jaeger (ref_66) 2004; 304 Peters (ref_51) 1994; 63 Garg (ref_62) 2017; 23 ref_1 Baena (ref_36) 2017; 9 Tanaka (ref_41) 1988; 272 ref_3 Gerogiannis (ref_10) 2010; 2 Chen (ref_37) 2018; 9 Chen (ref_2) 2017; 28 Chen (ref_48) 2019; 103 ref_4 ref_7 |
| References_xml | – ident: ref_24 doi: 10.1007/s40747-020-00130-x – volume: 2 start-page: 361 year: 2010 ident: ref_10 article-title: A case study for project and portfolio management information system selection: A group AHP-scoring model approach publication-title: Int. J. Proj. Organ. Manag. – ident: ref_3 doi: 10.1007/s12351-019-00489-x – volume: 103 start-page: 1721 year: 2019 ident: ref_48 article-title: An advanced IoT system for assisting ubiquitous manufacturing with 3D printing publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-019-03691-5 – volume: 28 start-page: 3507 year: 2017 ident: ref_2 article-title: New fuzzy method for improving the precision of productivity pre-dictions for a factory publication-title: Neural Comput. Appl. doi: 10.1007/s00521-016-2270-3 – volume: 16 start-page: 35 year: 2008 ident: ref_53 article-title: A fuzzy-neural system incorporating unequally important expert opinions for semiconductor yield forecasting publication-title: Int. J. Uncertain. Fuzziness Knowl.-Based Syst. doi: 10.1142/S0218488508005030 – volume: 40 start-page: 543 year: 2013 ident: ref_30 article-title: Group multi-criteria decision making based upon interval-valued fuzzy numbers: An extension of the MULTIMOORA method publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2012.07.066 – volume: 63 start-page: 45 year: 1994 ident: ref_51 article-title: Fuzzy linear regression with fuzzy intervals publication-title: Fuzzy Sets Syst. doi: 10.1016/0165-0114(94)90144-9 – ident: ref_39 doi: 10.1007/s00521-018-03988-8 – volume: 46 start-page: 43 year: 2013 ident: ref_60 article-title: Some hesitant interval-valued fuzzy aggregation operators and their applications to multiple attribute decision making publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2013.03.004 – volume: 275 start-page: 1954 year: 2018 ident: ref_56 article-title: Wilcoxon rank sum test drift detector publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.10.051 – volume: 5 start-page: 303 year: 2019 ident: ref_65 article-title: An advanced fuzzy collaborative intelligence approach for fitting the uncertain unit cost learning process publication-title: Complex Intell. Syst. doi: 10.1007/s40747-018-0081-0 – volume: 24 start-page: 384 year: 2016 ident: ref_31 article-title: An intervalvalued fuzzy number approach for supplier selection publication-title: J. Mar. Sci. Technol. – volume: 8 start-page: 7679 year: 2012 ident: ref_42 article-title: A collaborative fuzzy-neural system for global CO2 concentration forecasting publication-title: Int. J. Innov. Comput. Inf. Control – volume: 5 start-page: 41 year: 2019 ident: ref_64 article-title: Interval-valued Pythagorean fuzzy Einstein hybrid weighted averaging aggregation operator and their application to group decision making publication-title: Complex Intell. Syst. doi: 10.1007/s40747-018-0076-x – volume: 94 start-page: 106455 year: 2020 ident: ref_68 article-title: A fuzzy collaborative forecasting approach considering experts’ unequal levels of authority publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.106455 – ident: ref_1 – volume: 43 start-page: 21 year: 2013 ident: ref_28 article-title: Multi-criteria decision making method based on possibility degree of interval type-2 fuzzy number publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2012.11.007 – ident: ref_29 doi: 10.1109/WEIT.2011.19 – volume: 158 start-page: 2153 year: 2007 ident: ref_71 article-title: Incorporating fuzzy c-means and a back-propagation network ensemble to job completion time prediction in a semiconductor fabrication factory publication-title: Fuzzy Sets Syst. doi: 10.1016/j.fss.2007.04.013 – ident: ref_44 doi: 10.3390/sym10020045 – volume: 23 start-page: 546 year: 2017 ident: ref_62 article-title: Confidence levels based Pythagorean fuzzy aggregation operators and its application to decision-making process publication-title: Comput. Math. Organ. Theory doi: 10.1007/s10588-017-9242-8 – volume: 54 start-page: 2827 year: 2011 ident: ref_18 article-title: An overall profit Malmquist productivity index with fuzzy and interval data publication-title: Math. Comput. Model. doi: 10.1016/j.mcm.2011.07.003 – volume: 2 start-page: 20 year: 2007 ident: ref_34 article-title: Type-2 fuzzy sets and systems: An overview publication-title: IEEE Comput. Intell. Mag. – volume: 1 start-page: 1279 year: 2014 ident: ref_9 article-title: The role of different types of information systems in business organizations: A review publication-title: Int. J. Res. – volume: 60 start-page: 3480 year: 2011 ident: ref_12 article-title: State-of-the-art predictive maintenance techniques publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2009.2036347 – volume: 24 start-page: 511 year: 2014 ident: ref_15 article-title: Forecasting the productivity of a virtual enterprise by agent-based fuzzy collaborative intelligence—With Facebook as an example publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2014.08.003 – volume: 26 start-page: 1339 year: 2017 ident: ref_25 article-title: Multiobjective reliability redun-dancy allocation problem with interval type-2 fuzzy uncertainty publication-title: IEEE Trans. Fuzzy Syst. – volume: 11 start-page: 1213 year: 2020 ident: ref_32 article-title: Interval fuzzy number-based approach for modeling an uncertain fuzzy yield learning process publication-title: J. Ambient Intell. Humaniz. Comput. doi: 10.1007/s12652-019-01302-5 – ident: ref_54 doi: 10.1007/s10588-017-9262-4 – volume: 12 start-page: 809 year: 2019 ident: ref_67 article-title: Information structures in an incomplete interval-valued information system publication-title: Int. J. Comput. Intell. Syst. doi: 10.2991/ijcis.d.190712.001 – ident: ref_52 – volume: 98 start-page: 331 year: 1998 ident: ref_22 article-title: The applications of interval-valued fuzzy numbers and interval-distribution numbers publication-title: Fuzzy Sets Syst. doi: 10.1016/S0165-0114(96)00368-5 – volume: 31 start-page: 605 year: 2019 ident: ref_40 article-title: A multi-item transportation problem with mode of transportation preference by MCDM method in interval type-2 fuzzy environment publication-title: Neural Comput. Appl. doi: 10.1007/s00521-017-3093-6 – volume: 31 start-page: 343 year: 1989 ident: ref_59 article-title: Interval valued intuitionistic fuzzy sets publication-title: Fuzzy Sets Syst. doi: 10.1016/0165-0114(89)90205-4 – ident: ref_4 doi: 10.1007/s12351-019-00483-3 – ident: ref_11 doi: 10.1007/978-3-642-23960-1_9 – volume: 8 start-page: 301 year: 1975 ident: ref_16 article-title: The concept of a linguistic variable and its application to approximate reasoning—II publication-title: Inf. Sci. doi: 10.1016/0020-0255(75)90046-8 – volume: 47 start-page: 1311 year: 2001 ident: ref_5 article-title: Creating and transferring knowledge for productivity improvement in factories publication-title: Manag. Sci. – volume: 6 start-page: 164 year: 2011 ident: ref_8 article-title: The role of management information system (MIS) and Decision support system (DSS) for manager’s decision making process publication-title: Int. J. Bus. Manag. doi: 10.5539/ijbm.v6n7p164 – volume: 5 start-page: 234571 year: 2013 ident: ref_19 article-title: A fuzzy collaborative forecasting approach for forecasting the productivity of a factory publication-title: Adv. Mech. Eng. doi: 10.1155/2013/234571 – volume: 272 start-page: 275 year: 1988 ident: ref_41 article-title: Possibilistic linear systems and their application to the linear regression model publication-title: Fuzzy Sets Syst. doi: 10.1016/0165-0114(88)90054-1 – volume: 66 start-page: 476 year: 2013 ident: ref_6 article-title: A collaborative and artificial intelligence approach for semiconductor cost forecasting publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2013.07.014 – volume: 45 start-page: 54 year: 2002 ident: ref_63 article-title: Learning from productivity learning curves publication-title: Res. Technol. Manag doi: 10.1080/08956308.2002.11671501 – ident: ref_26 doi: 10.1007/s00500-019-04394-5 – volume: 32 start-page: 394 year: 2017 ident: ref_72 article-title: Feasibility evaluation and optimization of a smart manufacturing system based on 3D printing: A review publication-title: Int. J. Intell. Syst. doi: 10.1002/int.21866 – volume: 131 start-page: 455 year: 2019 ident: ref_35 article-title: An innovative yield learning model considering multiple learning sources and learning source interactions publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2018.07.002 – volume: 152 start-page: 455 year: 2005 ident: ref_17 article-title: A simple approximation of productivity scores of fuzzy production plans publication-title: Fuzzy Sets Syst. doi: 10.1016/j.fss.2004.10.022 – volume: 105 start-page: 4171 year: 2019 ident: ref_50 article-title: 3D printing technologies for enhancing the sustainability of an aircraft manufacturing or MRO company—A multi-expert partial consensus-FAHP analysis publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-019-03998-3 – ident: ref_7 doi: 10.5220/0007830700400050 – ident: ref_13 doi: 10.3386/w24001 – volume: 34 start-page: 3 year: 2019 ident: ref_27 article-title: Bonferroni means with induced ordered weighted average operators publication-title: Int. J. Intell. Syst. doi: 10.1002/int.22033 – volume: 9 start-page: 73 year: 2017 ident: ref_36 article-title: Learning factory: The path to industry 4.0 publication-title: Procedia Manuf. doi: 10.1016/j.promfg.2017.04.022 – volume: 9 start-page: 1013 year: 2018 ident: ref_37 article-title: An innovative fuzzy and artificial neural network approach for forecasting yield under an uncertain learning environment publication-title: J. Ambient Intell. Humaniz. Comput. doi: 10.1007/s12652-017-0504-6 – volume: 6 start-page: 9441 year: 2014 ident: ref_49 article-title: Enhancing the sustainability of a location-aware service through optimization publication-title: Sustainability doi: 10.3390/su6129441 – volume: 5 start-page: 371 year: 2019 ident: ref_23 article-title: Shortest path problem in fuzzy, intuitionistic fuzzy and neutrosophic environment: An overview publication-title: Complex Intell. Syst. doi: 10.1007/s40747-019-0098-z – volume: 22 start-page: 201 year: 2013 ident: ref_55 article-title: An agent-based fuzzy collaborative intelligence approach for precise and accurate semiconductor yield forecasting publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2013.2250290 – volume: 9 start-page: 1225 year: 2009 ident: ref_57 article-title: Fuzzy-neural approaches with example post-classification for estimating job cycle time in a wafer fab publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2009.03.006 – volume: 25 start-page: 85 year: 2019 ident: ref_21 article-title: A fuzzy polynomial fitting and mathematical programming approach for enhancing the accuracy and precision of productivity forecasting publication-title: Comput. Math. Organ. Theory doi: 10.1007/s10588-018-09287-w – ident: ref_38 doi: 10.1007/s00521-018-3492-3 – volume: 2 start-page: 157 year: 1991 ident: ref_14 article-title: Uncertainty and the measurement of productivity publication-title: J. Product. Anal. doi: 10.1007/BF00159729 – ident: ref_45 doi: 10.1007/978-3-642-17910-5 – ident: ref_61 doi: 10.1007/s12652-018-0912-2 – volume: 17 start-page: 110 year: 2018 ident: ref_70 article-title: A direct-solution fuzzy collaborative intelligence approach for yield forecasting in semiconductor manufacturing publication-title: Procedia Manuf. doi: 10.1016/j.promfg.2018.10.021 – ident: ref_43 – volume: 87 start-page: 1435 year: 2016 ident: ref_20 article-title: Evaluating sustainable advantages in productivity with a systematic procedure publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-013-5100-0 – volume: 234 start-page: 1044 year: 2020 ident: ref_47 article-title: A multibelief analytic hierarchy process and nonlinear programming approach for diversifying product designs: Smart backpack design as an example publication-title: Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. doi: 10.1177/0954405419896117 – volume: 20 start-page: 158 year: 2018 ident: ref_69 article-title: An integrative framework for collaborative forecasting in tourism supply chains publication-title: Int. J. Tour. Res. doi: 10.1002/jtr.2168 – volume: 304 start-page: 78 year: 2004 ident: ref_66 article-title: Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication publication-title: Science doi: 10.1126/science.1091277 – volume: 59 start-page: 597 year: 2019 ident: ref_33 article-title: Rankings and operations for interval type-2 fuzzy numbers: A review and some new methods publication-title: J. Appl. Math. Comput. doi: 10.1007/s12190-018-1193-9 – volume: 24 start-page: 441 year: 2018 ident: ref_46 article-title: Subjective stakeholder dynamics relationships treatment: A methodological approach using fuzzy decision-making publication-title: Comput. Math. Organ. Theory doi: 10.1007/s10588-018-09284-z – volume: 3 start-page: 102 year: 2017 ident: ref_58 article-title: Productivity forecast of a manufacturing sys-tem through intelligent modelling publication-title: Futo J. Ser. |
| SSID | ssj0000913849 |
| Score | 2.1952345 |
| Snippet | Existing methods for forecasting the productivity of a factory are subject to a major drawback—the lower and upper bounds of productivity are usually... |
| SourceID | doaj crossref |
| SourceType | Open Website Enrichment Source Index Database |
| StartPage | 998 |
| SubjectTerms | interval fuzzy number learning mixed binary quadratic programming ordered weighted average productivity |
| Title | Modeling an Uncertain Productivity Learning Process Using an Interval Fuzzy Methodology |
| URI | https://doaj.org/article/6894570f7b864c50a22e32f9fc07259a |
| Volume | 8 |
| WOSCitedRecordID | wos000554695400001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2227-7390 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2227-7390 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 2227-7390 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: K7- dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2227-7390 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: M7S dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2227-7390 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: BENPR dateStart: 20130301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content customDbUrl: eissn: 2227-7390 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: PIMPY dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3LSsQwFA0yuNCF-MTxRRazEsqkeTVZqszgwg6zcHB2pXmpIFXmITgLv90krUMFwY2bLkpSyr2hOSe99xwAepgZLozgSamQSWiKbFJigxKHsDScKqXqRuG7bDQS06kct6y-Qk1YLQ9cB67PhaQsQy5TglPNUImxJdhJp1HmoXuERh71tMhU_AbLlAgq64Y84nl93-O_J4G4B0TixxbUUuqPW8pwF-w0WBBe1e-wBzZstQ-287WQ6vwAPASvstAxDssKTnyC4g98OK51WqPxA2wkUh9hU_QPYxlAmBCP-_xSgsPlavUB8-gWHc_RD8FkOLi_uU0aL4REE8IXiVR-X8UKuZRqg4jxPMdYikvNLda0ZIaVimOrmSLEUzKXpY5Ga3QXWlUJI0egU71W9hhAnVIjdKYC1AmEScZ-U8w1slhohbvg8js8hW6EwoNfxUvhCUOIZdGKZRf01oPfan2M34ddhzivhwRR63jDp7poUl38leqT_3jIKdjCgTLHg5Qz0FnMlvYcbOr3xfN8dhFXkb_mn4MvhhDNEg |
| linkProvider | Directory of Open Access Journals |
| 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=Modeling+an+Uncertain+Productivity+Learning+Process+Using+an+Interval+Fuzzy+Methodology&rft.jtitle=Mathematics+%28Basel%29&rft.au=Chiu%2C+Min-Chi&rft.au=Chen%2C+Tin-Chih+Toly&rft.au=Hsu%2C+Keng-Wei&rft.date=2020-06-01&rft.issn=2227-7390&rft.eissn=2227-7390&rft.volume=8&rft.issue=6&rft.spage=998&rft_id=info:doi/10.3390%2Fmath8060998&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_math8060998 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2227-7390&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2227-7390&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2227-7390&client=summon |