A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem
•A novel fruit fly optimization algorithm is proposed for SFTSP.•Novel encoding and decoding methods are designed.•Effective smell-based, vision-based and cooperative searches are proposed.•Semiconductor final testing scheduling problem is solved effectively. In this paper, a novel fruit fly optimiz...
Gespeichert in:
| Veröffentlicht in: | Knowledge-based systems Jg. 57; S. 95 - 103 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.02.2014
|
| Schlagworte: | |
| ISSN: | 0950-7051, 1872-7409 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | •A novel fruit fly optimization algorithm is proposed for SFTSP.•Novel encoding and decoding methods are designed.•Effective smell-based, vision-based and cooperative searches are proposed.•Semiconductor final testing scheduling problem is solved effectively.
In this paper, a novel fruit fly optimization algorithm (nFOA) is proposed to solve the semiconductor final testing scheduling problem (SFTSP). First, a new encoding scheme is presented to represent solutions reasonably, and a new decoding scheme is presented to map solutions to feasible schedules. Second, it uses multiple fruit fly groups during the evolution process to enhance the parallel search ability of the FOA. According to the characteristics of the SFTSP, a smell-based search operator and a vision-based search operator are well designed for the groups to stress exploitation. Third, to simulate the information communication behavior among fruit flies, a cooperative search process is developed to stress exploration. The cooperative search process includes a modified improved precedence operation crossover (IPOX) and a modified multipoint preservative crossover (MPX) based on two popular structures of the flexible job shop scheduling. Moreover, the influence of the parameter setting is investigated by using Taguchi method of design-of-experiment (DOE), and suitable values are determined for key parameters. Finally, computational tests results with some benchmark instances and the comparisons to some existing algorithms are provided, which demonstrate the effectiveness and the efficiency of the nFOA in solving the SFTSP. |
|---|---|
| AbstractList | •A novel fruit fly optimization algorithm is proposed for SFTSP.•Novel encoding and decoding methods are designed.•Effective smell-based, vision-based and cooperative searches are proposed.•Semiconductor final testing scheduling problem is solved effectively.
In this paper, a novel fruit fly optimization algorithm (nFOA) is proposed to solve the semiconductor final testing scheduling problem (SFTSP). First, a new encoding scheme is presented to represent solutions reasonably, and a new decoding scheme is presented to map solutions to feasible schedules. Second, it uses multiple fruit fly groups during the evolution process to enhance the parallel search ability of the FOA. According to the characteristics of the SFTSP, a smell-based search operator and a vision-based search operator are well designed for the groups to stress exploitation. Third, to simulate the information communication behavior among fruit flies, a cooperative search process is developed to stress exploration. The cooperative search process includes a modified improved precedence operation crossover (IPOX) and a modified multipoint preservative crossover (MPX) based on two popular structures of the flexible job shop scheduling. Moreover, the influence of the parameter setting is investigated by using Taguchi method of design-of-experiment (DOE), and suitable values are determined for key parameters. Finally, computational tests results with some benchmark instances and the comparisons to some existing algorithms are provided, which demonstrate the effectiveness and the efficiency of the nFOA in solving the SFTSP. In this paper, a novel fruit fly optimization algorithm (nFOA) is proposed to solve the semiconductor final testing scheduling problem (SFTSP). First, a new encoding scheme is presented to represent solutions reasonably, and a new decoding scheme is presented to map solutions to feasible schedules. Second, it uses multiple fruit fly groups during the evolution process to enhance the parallel search ability of the FOA. According to the characteristics of the SFTSP, a smell-based search operator and a vision-based search operator are well designed for the groups to stress exploitation. Third, to simulate the information communication behavior among fruit flies, a cooperative search process is developed to stress exploration. The cooperative search process includes a modified improved precedence operation crossover (IPOX) and a modified multipoint preservative crossover (MPX) based on two popular structures of the flexible job shop scheduling. Moreover, the influence of the parameter setting is investigated by using Taguchi method of design-of-experiment (DOE), and suitable values are determined for key parameters. Finally, computational tests results with some benchmark instances and the comparisons to some existing algorithms are provided, which demonstrate the effectiveness and the efficiency of the nFOA in solving the SFTSP. |
| Author | Wang, Sheng-yao Zheng, Xiao-long Wang, Ling |
| Author_xml | – sequence: 1 givenname: Xiao-long surname: Zheng fullname: Zheng, Xiao-long – sequence: 2 givenname: Ling surname: Wang fullname: Wang, Ling email: wangling@mail.tsinghua.edu.cn – sequence: 3 givenname: Sheng-yao surname: Wang fullname: Wang, Sheng-yao |
| BookMark | eNqFkEFP4zAQha0VSLTAP-Dg416a9ThpnOwBqapgQULisnu2HGdC3XXsYjuVur8el3LiwJ5mNJr35s03J2fOOyTkBlgBDOof2-Kv8_EQC86gLIAXDOAbmUEj-EJUrD0jM9Yu2UKwJVyQeYxbxhjn0MyIXFHn92jpECaT6GAP1O-SGc0_lYx3VNkXH0zajHTwgaYN0oij0d71k055MhinLE0Yk3EvNOoN9pM9trvgO4vjFTkflI14_VEvyZ_7u9_rh8XT86_H9eppocuyTTllVXadwBxTKNHXrBXlUA-i7xTv-korxgGW2KJqVJX_qsuGIwihelYBlKK8JN9Pvvnu65TjyNFEjdYqh36KEpa1yOYVb_JqdVrVwccYcJC7YEYVDhKYPPKUW3niKY88JXCZeWbZz08ybdI7pBSUsf8T357EmBnsDQYZtUGnsTcBdZK9N18bvAEDhJeY |
| CitedBy_id | crossref_primary_10_1007_s13042_017_0669_5 crossref_primary_10_1108_BIJ_05_2018_0120 crossref_primary_10_1142_S0218001417540155 crossref_primary_10_1016_j_knosys_2017_09_038 crossref_primary_10_1016_j_knosys_2015_09_032 crossref_primary_10_1016_j_swevo_2017_06_001 crossref_primary_10_1007_s13369_015_1905_5 crossref_primary_10_1016_j_eswa_2020_113486 crossref_primary_10_1016_j_knosys_2020_106437 crossref_primary_10_1109_ACCESS_2019_2917502 crossref_primary_10_1007_s11424_018_7250_5 crossref_primary_10_1016_j_jclepro_2018_03_149 crossref_primary_10_1080_0305215X_2021_1949007 crossref_primary_10_1016_j_knosys_2016_01_006 crossref_primary_10_1155_2014_314797 crossref_primary_10_1016_j_knosys_2015_07_027 crossref_primary_10_1007_s00521_020_05657_1 crossref_primary_10_1016_j_eswa_2016_08_039 crossref_primary_10_1016_j_knosys_2016_01_002 crossref_primary_10_1016_j_knosys_2020_106704 crossref_primary_10_1155_2020_6315675 crossref_primary_10_1007_s00158_020_02589_1 crossref_primary_10_1016_j_amc_2017_01_004 crossref_primary_10_1016_j_swevo_2017_05_007 crossref_primary_10_1007_s40031_022_00725_7 crossref_primary_10_1007_s00366_020_01174_w crossref_primary_10_3390_app6100294 crossref_primary_10_1088_1742_6596_1175_1_012042 crossref_primary_10_1007_s00500_015_1641_5 crossref_primary_10_1016_j_ins_2016_08_046 crossref_primary_10_3390_app6100299 crossref_primary_10_1007_s00500_017_2733_1 crossref_primary_10_1007_s10462_021_10105_0 crossref_primary_10_1371_journal_pone_0165804 crossref_primary_10_1016_j_eswa_2024_125424 crossref_primary_10_1080_00207543_2022_2075292 crossref_primary_10_1016_j_cie_2020_106280 crossref_primary_10_1080_00207543_2016_1170226 crossref_primary_10_1109_TSMC_2016_2616347 crossref_primary_10_1007_s00500_020_05141_x crossref_primary_10_1016_j_eswa_2021_115978 crossref_primary_10_1109_ACCESS_2018_2867728 crossref_primary_10_1016_j_cie_2022_108603 crossref_primary_10_1016_j_eswa_2021_115214 crossref_primary_10_1109_ACCESS_2018_2872110 crossref_primary_10_1155_2020_9676279 crossref_primary_10_1016_j_sasc_2025_200275 crossref_primary_10_1007_s11042_016_4233_1 crossref_primary_10_4018_JGIM_351156 crossref_primary_10_1016_j_energy_2021_120153 crossref_primary_10_3390_a11120210 crossref_primary_10_1515_jag_2019_0025 crossref_primary_10_1016_j_eswa_2015_01_048 crossref_primary_10_1016_j_cie_2023_109423 crossref_primary_10_3390_s20185026 crossref_primary_10_1016_j_cie_2024_110259 crossref_primary_10_1016_j_engappai_2023_106864 crossref_primary_10_1016_j_swevo_2019_01_002 crossref_primary_10_1080_0305215X_2019_1624738 crossref_primary_10_1016_j_eswa_2020_113502 crossref_primary_10_1007_s10586_023_04127_2 crossref_primary_10_1016_j_jclepro_2017_09_037 crossref_primary_10_3390_en12173260 crossref_primary_10_1016_j_knosys_2015_03_024 crossref_primary_10_3390_app9142879 crossref_primary_10_1016_j_knosys_2015_08_010 crossref_primary_10_1016_j_tre_2024_103727 crossref_primary_10_1016_j_jmsy_2022_01_014 crossref_primary_10_1049_iet_gtd_2015_0284 crossref_primary_10_1016_j_cie_2020_106781 crossref_primary_10_1016_j_asoc_2022_109235 crossref_primary_10_1016_j_cie_2015_05_022 crossref_primary_10_1109_ACCESS_2019_2940104 crossref_primary_10_1155_2021_8571524 crossref_primary_10_1007_s10845_016_1215_0 crossref_primary_10_1016_j_cor_2021_105619 crossref_primary_10_1016_j_knosys_2016_09_027 crossref_primary_10_1007_s11047_016_9604_z crossref_primary_10_1007_s00500_015_1890_3 crossref_primary_10_1016_j_asoc_2018_05_030 crossref_primary_10_1155_2015_862185 crossref_primary_10_1108_IJPCC_08_2020_0101 crossref_primary_10_1016_j_asoc_2016_11_023 crossref_primary_10_1016_j_knosys_2019_03_028 crossref_primary_10_3390_su11041119 crossref_primary_10_1109_TAP_2018_2800695 crossref_primary_10_3390_a7030363 crossref_primary_10_4018_IJISSCM_2019040103 crossref_primary_10_1016_j_knosys_2014_08_022 crossref_primary_10_1080_23249935_2015_1099576 crossref_primary_10_1049_cim2_12005 crossref_primary_10_1016_j_jenvman_2024_122361 crossref_primary_10_1080_00207543_2016_1177671 crossref_primary_10_1016_j_isatra_2015_03_013 crossref_primary_10_1016_j_jestch_2024_101765 crossref_primary_10_1109_TASE_2018_2862380 |
| Cites_doi | 10.1080/09540091.2013.854735 10.1109/4235.585892 10.1016/j.eswa.2007.07.026 10.1007/s00521-011-0769-1 10.1109/66.705373 10.1109/66.806130 10.1007/s10845-011-0570-0 10.4156/jcit.vol7.issue16.9 10.1016/j.ejor.2011.05.052 10.1080/0020754032000123588 10.1007/s10845-013-0746-x 10.1016/S0925-5273(03)00186-5 10.1007/BF00170018 10.1016/j.knosys.2011.07.001 10.1007/s11071-013-0814-y 10.1109/66.97809 10.1016/j.cie.2011.12.014 10.1007/s00170-011-3610-1 10.1007/978-3-642-04944-6_14 10.1080/00207543.2012.752588 10.1007/s00170-010-2642-2 |
| ContentType | Journal Article |
| Copyright | 2013 Elsevier B.V. |
| Copyright_xml | – notice: 2013 Elsevier B.V. |
| DBID | AAYXX CITATION 7SC 7U5 8FD JQ2 L7M L~C L~D |
| DOI | 10.1016/j.knosys.2013.12.011 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Solid State and Superconductivity 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 Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest Computer Science Collection Computer and Information Systems Abstracts Solid State and Superconductivity Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Technology Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-7409 |
| EndPage | 103 |
| ExternalDocumentID | 10_1016_j_knosys_2013_12_011 S0950705113003912 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 29L 4.4 457 4G. 5VS 7-5 71M 77K 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABAOU ABBOA ABIVO ABJNI ABMAC ABXDB ABYKQ ACAZW ACDAQ ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADGUI ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ARUGR ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q G8K GBLVA GBOLZ HLZ HVGLF HZ~ IHE J1W JJJVA KOM LG9 LY7 M41 MHUIS MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SES SET SEW SPC SPCBC SST SSV SSW SSZ T5K UHS WH7 WUQ XPP ZMT ~02 ~G- 77I 9DU AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 7SC 7U5 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c339t-7443bb7e0957a7d60973f6f7dba2bd4ca02115e9ea8a41876382e177ad0411373 |
| ISICitedReferencesCount | 127 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000331781300008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0950-7051 |
| IngestDate | Thu Oct 02 10:03:49 EDT 2025 Tue Nov 18 21:22:41 EST 2025 Sat Nov 29 06:41:25 EST 2025 Fri Feb 23 02:28:22 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Vision-based search Smell-based search Semiconductor final testing scheduling problem Fruit fly optimization algorithm Cooperative search |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c339t-7443bb7e0957a7d60973f6f7dba2bd4ca02115e9ea8a41876382e177ad0411373 |
| Notes | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
| PQID | 1567095428 |
| PQPubID | 23500 |
| PageCount | 9 |
| ParticipantIDs | proquest_miscellaneous_1567095428 crossref_primary_10_1016_j_knosys_2013_12_011 crossref_citationtrail_10_1016_j_knosys_2013_12_011 elsevier_sciencedirect_doi_10_1016_j_knosys_2013_12_011 |
| PublicationCentury | 2000 |
| PublicationDate | February 2014 2014-02-00 20140201 |
| PublicationDateYYYYMMDD | 2014-02-01 |
| PublicationDate_xml | – month: 02 year: 2014 text: February 2014 |
| PublicationDecade | 2010 |
| PublicationTitle | Knowledge-based systems |
| PublicationYear | 2014 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | T.R. Chen, T.C. Hsia, Job shop scheduling with multiple resources and an application to a semiconductor testing facility, in: IEEE Proceedings of the 33rd Conference on Decision and Control, 1994, pp. 1564–1570. Wang, Wang, Xu, Zhou, Liu (b0080) 2012; 62 Wu, Hao, Chien, Gen (b0050) 2012; 23 Uzso, Martin-Vega, Lee, Leonard (b0020) 1991; 4 Li, Xu, Li, Hu (b0070) 2012; 7 Sheng, Bao (b0075) 2013; 73 Wang, Zhou, Xu, Wang, Liu (b0090) 2012; 60 Pan (b0055) 2012; 26 Zhang, Zheng, Hou, Li (b0010) 2011; 215 Xiong, Zhou (b0035) 1998; 11 Wang, Gao, Zhang, Shao (b0110) 2010; 51 Hao, Wu, Chien, Gen (b0015) 2013 Pearn, Chun, Chen, Yang (b0095) 2004; 88 Dorigo, Gambardella (b0100) 1997; 1 Freed, Leachman (b0040) 1999; 12 Pan (b0065) 2013; 25 Wang, Wang, Liu (b0085) 2013; 51 Montgomery (b0115) 2005 X.S. Yang, Firefly algorithms for multimodal optimization, in: Stochastic Algorithms: Foundations and Applications, SAGA 2009, Lecture Notes in Computer Sciences, vol. 5792, 2009, pp. 169–178. Ovacik, Uzsoy (b0030) 1996; 8 Wu, Chien (b0005) 2008; 35 Lin, Wang, Lee (b0045) 2004; 42 Lin (b0060) 2013; 22 Uzso (10.1016/j.knosys.2013.12.011_b0020) 1991; 4 10.1016/j.knosys.2013.12.011_b0105 10.1016/j.knosys.2013.12.011_b0025 Lin (10.1016/j.knosys.2013.12.011_b0045) 2004; 42 Sheng (10.1016/j.knosys.2013.12.011_b0075) 2013; 73 Dorigo (10.1016/j.knosys.2013.12.011_b0100) 1997; 1 Zhang (10.1016/j.knosys.2013.12.011_b0010) 2011; 215 Ovacik (10.1016/j.knosys.2013.12.011_b0030) 1996; 8 Xiong (10.1016/j.knosys.2013.12.011_b0035) 1998; 11 Wu (10.1016/j.knosys.2013.12.011_b0050) 2012; 23 Wang (10.1016/j.knosys.2013.12.011_b0080) 2012; 62 Montgomery (10.1016/j.knosys.2013.12.011_b0115) 2005 Wang (10.1016/j.knosys.2013.12.011_b0085) 2013; 51 Pearn (10.1016/j.knosys.2013.12.011_b0095) 2004; 88 Hao (10.1016/j.knosys.2013.12.011_b0015) 2013 Freed (10.1016/j.knosys.2013.12.011_b0040) 1999; 12 Li (10.1016/j.knosys.2013.12.011_b0070) 2012; 7 Wang (10.1016/j.knosys.2013.12.011_b0110) 2010; 51 Lin (10.1016/j.knosys.2013.12.011_b0060) 2013; 22 Wu (10.1016/j.knosys.2013.12.011_b0005) 2008; 35 Pan (10.1016/j.knosys.2013.12.011_b0065) 2013; 25 Pan (10.1016/j.knosys.2013.12.011_b0055) 2012; 26 Wang (10.1016/j.knosys.2013.12.011_b0090) 2012; 60 |
| References_xml | – volume: 62 start-page: 917 year: 2012 end-page: 926 ident: b0080 article-title: A bi-population based estimation of distribution algorithm for the flexible job-shop scheduling problem publication-title: Comput. Indust. Eng. – volume: 8 start-page: 357 year: 1996 end-page: 388 ident: b0030 article-title: Decomposition methods for scheduling semiconductor testing facilities publication-title: Int. J. Flex. Manuf. Syst. – volume: 42 start-page: 79 year: 2004 end-page: 99 ident: b0045 article-title: Capacity-constrained scheduling for a logic IC final test facility publication-title: Int. J. Product. Res. – volume: 12 start-page: 523 year: 1999 end-page: 530 ident: b0040 article-title: Scheduling semiconductor device test operations on multihead testers publication-title: IEEE Trans. Semiconduct. Manuf. – volume: 11 start-page: 384 year: 1998 end-page: 393 ident: b0035 article-title: Scheduling of semiconductor test facility via Petri nets and hybrid heuristic search publication-title: IEEE Trans. Semiconduct. Manuf. – volume: 215 start-page: 446 year: 2011 end-page: 458 ident: b0010 article-title: Semiconductor final test scheduling with Sarsa(λ, k) algorithm publication-title: Euro. J. Operat. Res. – reference: T.R. Chen, T.C. Hsia, Job shop scheduling with multiple resources and an application to a semiconductor testing facility, in: IEEE Proceedings of the 33rd Conference on Decision and Control, 1994, pp. 1564–1570. – volume: 51 start-page: 757 year: 2010 end-page: 767 ident: b0110 article-title: A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem publication-title: Int. J. Advan. Manuf. Technol. – volume: 51 start-page: 3574 year: 2013 end-page: 3592 ident: b0085 article-title: A Pareto-based estimation of distribution algorithm for the multi-objective flexible job-shop scheduling problem publication-title: Int. J. Product. Res. – year: 2013 ident: b0015 article-title: The cooperative estimation of distribution algorithm: a novel approach for semiconductor final test scheduling problems publication-title: J. Intell. Manuf. – volume: 22 start-page: 783 year: 2013 end-page: 791 ident: b0060 article-title: Analysis of service satisfaction in web auction logistics service using a combination of fruit fly optimization algorithm and general regression neural network publication-title: Neural Comput. Appl. – volume: 25 start-page: 151 year: 2013 end-page: 160 ident: b0065 article-title: Using modified fruit fly optimisation algorithm to perform the function test and case studies publication-title: Connect. Sci. – volume: 26 start-page: 69 year: 2012 end-page: 74 ident: b0055 article-title: A new fruit fly optimization algorithm: taking the financial distress model as an example publication-title: Knowl.-Based Syst. – volume: 35 start-page: 485 year: 2008 end-page: 496 ident: b0005 article-title: Modeling semiconductor testing job scheduling and dynamic testing machine configuration publication-title: Expert Syst. Appl. – volume: 1 start-page: 53 year: 1997 end-page: 66 ident: b0100 article-title: LM, Ant colony system: a cooperative learning approach to the traveling salesman problem publication-title: IEEE Trans. Evolution. Comput. – year: 2005 ident: b0115 article-title: Design and Analysis of Experiments – volume: 23 start-page: 2255 year: 2012 end-page: 2270 ident: b0050 article-title: A novel bi-vector encoding genetic algorithm for the simultaneous multiple resources scheduling problem publication-title: J. Intell. Manuf. – volume: 60 start-page: 303 year: 2012 end-page: 315 ident: b0090 article-title: An effective artificial bee colony algorithm for the flexible job-shop scheduling problem publication-title: Int. J. Advan. Manuf. Technol. – reference: X.S. Yang, Firefly algorithms for multimodal optimization, in: Stochastic Algorithms: Foundations and Applications, SAGA 2009, Lecture Notes in Computer Sciences, vol. 5792, 2009, pp. 169–178. – volume: 73 start-page: 611 year: 2013 end-page: 619 ident: b0075 article-title: Fruit fly optimization algorithm based fractional order fuzzy-PID controller for electronic throttle publication-title: Nonlin. Dynam. – volume: 88 start-page: 257 year: 2004 end-page: 267 ident: b0095 article-title: A case study on the multistage IC final testing scheduling problem with reentry publication-title: Int. J. Product. Econ. – volume: 4 start-page: 270 year: 1991 end-page: 280 ident: b0020 article-title: Production scheduling algorithms for a semiconductor test facility publication-title: IEEE Trans. Semiconduct. Manuf. – volume: 7 start-page: 69 year: 2012 end-page: 77 ident: b0070 article-title: A novel modified fly optimization algorithm for designing the self-tuning proportional integral derivative controller publication-title: J. Converge. Inform. Technol. – volume: 25 start-page: 151 issue: 2–3 year: 2013 ident: 10.1016/j.knosys.2013.12.011_b0065 article-title: Using modified fruit fly optimisation algorithm to perform the function test and case studies publication-title: Connect. Sci. doi: 10.1080/09540091.2013.854735 – volume: 1 start-page: 53 issue: 1 year: 1997 ident: 10.1016/j.knosys.2013.12.011_b0100 article-title: LM, Ant colony system: a cooperative learning approach to the traveling salesman problem publication-title: IEEE Trans. Evolution. Comput. doi: 10.1109/4235.585892 – volume: 35 start-page: 485 issue: 1–2 year: 2008 ident: 10.1016/j.knosys.2013.12.011_b0005 article-title: Modeling semiconductor testing job scheduling and dynamic testing machine configuration publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2007.07.026 – volume: 22 start-page: 783 year: 2013 ident: 10.1016/j.knosys.2013.12.011_b0060 article-title: Analysis of service satisfaction in web auction logistics service using a combination of fruit fly optimization algorithm and general regression neural network publication-title: Neural Comput. Appl. doi: 10.1007/s00521-011-0769-1 – volume: 11 start-page: 384 issue: 3 year: 1998 ident: 10.1016/j.knosys.2013.12.011_b0035 article-title: Scheduling of semiconductor test facility via Petri nets and hybrid heuristic search publication-title: IEEE Trans. Semiconduct. Manuf. doi: 10.1109/66.705373 – ident: 10.1016/j.knosys.2013.12.011_b0025 – volume: 12 start-page: 523 issue: 4 year: 1999 ident: 10.1016/j.knosys.2013.12.011_b0040 article-title: Scheduling semiconductor device test operations on multihead testers publication-title: IEEE Trans. Semiconduct. Manuf. doi: 10.1109/66.806130 – volume: 23 start-page: 2255 issue: 6 year: 2012 ident: 10.1016/j.knosys.2013.12.011_b0050 article-title: A novel bi-vector encoding genetic algorithm for the simultaneous multiple resources scheduling problem publication-title: J. Intell. Manuf. doi: 10.1007/s10845-011-0570-0 – volume: 7 start-page: 69 year: 2012 ident: 10.1016/j.knosys.2013.12.011_b0070 article-title: A novel modified fly optimization algorithm for designing the self-tuning proportional integral derivative controller publication-title: J. Converge. Inform. Technol. doi: 10.4156/jcit.vol7.issue16.9 – volume: 215 start-page: 446 issue: 2 year: 2011 ident: 10.1016/j.knosys.2013.12.011_b0010 article-title: Semiconductor final test scheduling with Sarsa(λ, k) algorithm publication-title: Euro. J. Operat. Res. doi: 10.1016/j.ejor.2011.05.052 – volume: 42 start-page: 79 issue: 1 year: 2004 ident: 10.1016/j.knosys.2013.12.011_b0045 article-title: Capacity-constrained scheduling for a logic IC final test facility publication-title: Int. J. Product. Res. doi: 10.1080/0020754032000123588 – year: 2013 ident: 10.1016/j.knosys.2013.12.011_b0015 article-title: The cooperative estimation of distribution algorithm: a novel approach for semiconductor final test scheduling problems publication-title: J. Intell. Manuf. doi: 10.1007/s10845-013-0746-x – volume: 88 start-page: 257 issue: 3 year: 2004 ident: 10.1016/j.knosys.2013.12.011_b0095 article-title: A case study on the multistage IC final testing scheduling problem with reentry publication-title: Int. J. Product. Econ. doi: 10.1016/S0925-5273(03)00186-5 – volume: 8 start-page: 357 issue: 4 year: 1996 ident: 10.1016/j.knosys.2013.12.011_b0030 article-title: Decomposition methods for scheduling semiconductor testing facilities publication-title: Int. J. Flex. Manuf. Syst. doi: 10.1007/BF00170018 – volume: 26 start-page: 69 year: 2012 ident: 10.1016/j.knosys.2013.12.011_b0055 article-title: A new fruit fly optimization algorithm: taking the financial distress model as an example publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2011.07.001 – volume: 73 start-page: 611 issue: 1–2 year: 2013 ident: 10.1016/j.knosys.2013.12.011_b0075 article-title: Fruit fly optimization algorithm based fractional order fuzzy-PID controller for electronic throttle publication-title: Nonlin. Dynam. doi: 10.1007/s11071-013-0814-y – volume: 4 start-page: 270 issue: 4 year: 1991 ident: 10.1016/j.knosys.2013.12.011_b0020 article-title: Production scheduling algorithms for a semiconductor test facility publication-title: IEEE Trans. Semiconduct. Manuf. doi: 10.1109/66.97809 – volume: 62 start-page: 917 issue: 4 year: 2012 ident: 10.1016/j.knosys.2013.12.011_b0080 article-title: A bi-population based estimation of distribution algorithm for the flexible job-shop scheduling problem publication-title: Comput. Indust. Eng. doi: 10.1016/j.cie.2011.12.014 – volume: 60 start-page: 303 issue: 1–4 year: 2012 ident: 10.1016/j.knosys.2013.12.011_b0090 article-title: An effective artificial bee colony algorithm for the flexible job-shop scheduling problem publication-title: Int. J. Advan. Manuf. Technol. doi: 10.1007/s00170-011-3610-1 – year: 2005 ident: 10.1016/j.knosys.2013.12.011_b0115 – ident: 10.1016/j.knosys.2013.12.011_b0105 doi: 10.1007/978-3-642-04944-6_14 – volume: 51 start-page: 3574 issue: 12 year: 2013 ident: 10.1016/j.knosys.2013.12.011_b0085 article-title: A Pareto-based estimation of distribution algorithm for the multi-objective flexible job-shop scheduling problem publication-title: Int. J. Product. Res. doi: 10.1080/00207543.2012.752588 – volume: 51 start-page: 757 issue: 5–8 year: 2010 ident: 10.1016/j.knosys.2013.12.011_b0110 article-title: A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem publication-title: Int. J. Advan. Manuf. Technol. doi: 10.1007/s00170-010-2642-2 |
| SSID | ssj0002218 |
| Score | 2.4227664 |
| Snippet | •A novel fruit fly optimization algorithm is proposed for SFTSP.•Novel encoding and decoding methods are designed.•Effective smell-based, vision-based and... In this paper, a novel fruit fly optimization algorithm (nFOA) is proposed to solve the semiconductor final testing scheduling problem (SFTSP). First, a new... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 95 |
| SubjectTerms | Algorithms Cooperative search Crossovers Fruit fly optimization algorithm Fruits Mathematical models Operators Search process Searching Semiconductor final testing scheduling problem Semiconductors Smell-based search Vision-based search |
| Title | A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem |
| URI | https://dx.doi.org/10.1016/j.knosys.2013.12.011 https://www.proquest.com/docview/1567095428 |
| Volume | 57 |
| WOSCitedRecordID | wos000331781300008&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: 1872-7409 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002218 issn: 0950-7051 databaseCode: AIEXJ dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELZQy4ELb0R5yUjcoqDYceL4uEJFPKoKiYJWXCwnsWlKGle72ar994wdJ912BaUHLlHkjS1758vMZDz-BqE3ulB1zpMqro3bZsxLE4sKlKGhwiRZyarMl2T5vsf394v5XHwJ2wVLX06Ad11xdiZO_quooQ2E7Y7O3kDc06DQAPcgdLiC2OH6T4KfRZ091W1kFqumj0x7HllQC8fhvGWk2p920fSHx1OC4dLlx9vOEb9Ci_FlsnpHvuEiDSDTehWOrPvSM-ve7OcxIBc7Y1gHWujJS_9xqAdNMm-UjVsbjKQP3w8_7DWbbV9dr_hc2fWABGFjDvNaZDGJeRJ4ZIOSHViog5YcymoGe0s8x8GmKh-iCkdvf3UWZu-S8FIfuA26-RJz9hWLNuUZjilsR3IYRbpRJKEyccfBtynPBCjz7dnH3fmnyX5T6qPC0zLGA5c-K3BzNn9yaK6Ydu-vHNxHd8OHBp4NAHmAbunuIbo3FvHAQac_QnKGPV6wxwsGvOB1vOAJLxjwggEv-BJesMcLDnjBF3jBAS-P0bf3uwfvPsSh6kZcpanoY85YWpZcw_q54nXu-JxMbnhdKlrWrFLgFZJMC60KxYgjNCyoJpyrOmGEpDx9grY62-mnCFMBzipVIi9Kw-C1V6QqUkK14IIWNWM7KB3_OlkFSnpXGaWVfxPcDoqnXicDJcs1z_NRKjK4lYO7KAFq1_R8PQpRgtZ1W2mq03a1lCRzvIewuOLZDWfzHN25eGleoK1-sdIv0e3qtG-Wi1cBi78BMF2rWg |
| 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+novel+fruit+fly+optimization+algorithm+for+the+semiconductor+final+testing+scheduling+problem&rft.jtitle=Knowledge-based+systems&rft.au=Zheng%2C+Xiao-long&rft.au=Wang%2C+Ling&rft.au=Wang%2C+Sheng-yao&rft.date=2014-02-01&rft.issn=0950-7051&rft.volume=57&rft.spage=95&rft.epage=103&rft_id=info:doi/10.1016%2Fj.knosys.2013.12.011&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_knosys_2013_12_011 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0950-7051&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0950-7051&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0950-7051&client=summon |