A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model
Abstract This paper aims to develop a fuzzy-based solution approach to address a machine-loading problem of a flexible manufacturing system (FMS). The proposed solution methodology effectively deals with all the three main constituents of a machine loading problem, viz. job sequence determination, o...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kumar, Rajeev Ranjan [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2004 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer-Verlag London Limited 2004 |
|---|
| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - Springer-Verlag, 1985, 23(2004), 5-6 vom: 22. Jan., Seite 318-341 |
|---|---|
| Übergeordnetes Werk: |
volume:23 ; year:2004 ; number:5-6 ; day:22 ; month:01 ; pages:318-341 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00170-002-1499-4 |
|---|
| Katalog-ID: |
OLC2025998805 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2025998805 | ||
| 003 | DE-627 | ||
| 005 | 20230401073409.0 | ||
| 007 | tu | ||
| 008 | 200820s2004 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00170-002-1499-4 |2 doi | |
| 035 | |a (DE-627)OLC2025998805 | ||
| 035 | |a (DE-He213)s00170-002-1499-4-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 670 |q VZ |
| 100 | 1 | |a Kumar, Rajeev Ranjan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model |
| 264 | 1 | |c 2004 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer-Verlag London Limited 2004 | ||
| 520 | |a Abstract This paper aims to develop a fuzzy-based solution approach to address a machine-loading problem of a flexible manufacturing system (FMS). The proposed solution methodology effectively deals with all the three main constituents of a machine loading problem, viz. job sequence determination, operation machine allocation and the reallocation of jobs. The main objectives of the FMS loading problem considered here are minimisation of system imbalance and maximisation of throughput; the constraints to be satisfied are the available machining time and tool slots. An analytical argument has been provided to support the membership function related to the operation machine allocation vector. Computational results revealed the superiority of the proposed algorithm over other heuristics when it is tested on a standard data set adopted from literature. A new class of petri net model called the “Extended neuro fuzzy petri net” is constructed to capture clearly the various details of the machine loading problem which can be further extended to learn from experience and perform inferences so that truly intelligent system characteristics can be realised. | ||
| 650 | 4 | |a FMS | |
| 650 | 4 | |a Machine loading | |
| 650 | 4 | |a Fuzzy logic | |
| 650 | 4 | |a Petri net | |
| 650 | 4 | |a Neuro fuzzy Petri net | |
| 700 | 1 | |a Singh, Amarjit Kumar |4 aut | |
| 700 | 1 | |a Tiwari, M. K. |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t The international journal of advanced manufacturing technology |d Springer-Verlag, 1985 |g 23(2004), 5-6 vom: 22. Jan., Seite 318-341 |w (DE-627)129185299 |w (DE-600)52651-4 |w (DE-576)014456192 |x 0268-3768 |7 nnns |
| 773 | 1 | 8 | |g volume:23 |g year:2004 |g number:5-6 |g day:22 |g month:01 |g pages:318-341 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s00170-002-1499-4 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_21 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_150 | ||
| 912 | |a GBV_ILN_2006 | ||
| 912 | |a GBV_ILN_2014 | ||
| 912 | |a GBV_ILN_2018 | ||
| 912 | |a GBV_ILN_2241 | ||
| 912 | |a GBV_ILN_2333 | ||
| 912 | |a GBV_ILN_4046 | ||
| 912 | |a GBV_ILN_4277 | ||
| 912 | |a GBV_ILN_4307 | ||
| 951 | |a AR | ||
| 952 | |d 23 |j 2004 |e 5-6 |b 22 |c 01 |h 318-341 | ||
| author_variant |
r r k rr rrk a k s ak aks m k t mk mkt |
|---|---|
| matchkey_str |
article:02683768:2004----::fzyaeagrtmoovteahnlaigrbesffsni |
| hierarchy_sort_str |
2004 |
| publishDate |
2004 |
| allfields |
10.1007/s00170-002-1499-4 doi (DE-627)OLC2025998805 (DE-He213)s00170-002-1499-4-p DE-627 ger DE-627 rakwb eng 670 VZ Kumar, Rajeev Ranjan verfasserin aut A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model 2004 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2004 Abstract This paper aims to develop a fuzzy-based solution approach to address a machine-loading problem of a flexible manufacturing system (FMS). The proposed solution methodology effectively deals with all the three main constituents of a machine loading problem, viz. job sequence determination, operation machine allocation and the reallocation of jobs. The main objectives of the FMS loading problem considered here are minimisation of system imbalance and maximisation of throughput; the constraints to be satisfied are the available machining time and tool slots. An analytical argument has been provided to support the membership function related to the operation machine allocation vector. Computational results revealed the superiority of the proposed algorithm over other heuristics when it is tested on a standard data set adopted from literature. A new class of petri net model called the “Extended neuro fuzzy petri net” is constructed to capture clearly the various details of the machine loading problem which can be further extended to learn from experience and perform inferences so that truly intelligent system characteristics can be realised. FMS Machine loading Fuzzy logic Petri net Neuro fuzzy Petri net Singh, Amarjit Kumar aut Tiwari, M. K. aut Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 23(2004), 5-6 vom: 22. Jan., Seite 318-341 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:23 year:2004 number:5-6 day:22 month:01 pages:318-341 https://doi.org/10.1007/s00170-002-1499-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_21 GBV_ILN_23 GBV_ILN_70 GBV_ILN_150 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2018 GBV_ILN_2241 GBV_ILN_2333 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4307 AR 23 2004 5-6 22 01 318-341 |
| spelling |
10.1007/s00170-002-1499-4 doi (DE-627)OLC2025998805 (DE-He213)s00170-002-1499-4-p DE-627 ger DE-627 rakwb eng 670 VZ Kumar, Rajeev Ranjan verfasserin aut A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model 2004 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2004 Abstract This paper aims to develop a fuzzy-based solution approach to address a machine-loading problem of a flexible manufacturing system (FMS). The proposed solution methodology effectively deals with all the three main constituents of a machine loading problem, viz. job sequence determination, operation machine allocation and the reallocation of jobs. The main objectives of the FMS loading problem considered here are minimisation of system imbalance and maximisation of throughput; the constraints to be satisfied are the available machining time and tool slots. An analytical argument has been provided to support the membership function related to the operation machine allocation vector. Computational results revealed the superiority of the proposed algorithm over other heuristics when it is tested on a standard data set adopted from literature. A new class of petri net model called the “Extended neuro fuzzy petri net” is constructed to capture clearly the various details of the machine loading problem which can be further extended to learn from experience and perform inferences so that truly intelligent system characteristics can be realised. FMS Machine loading Fuzzy logic Petri net Neuro fuzzy Petri net Singh, Amarjit Kumar aut Tiwari, M. K. aut Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 23(2004), 5-6 vom: 22. Jan., Seite 318-341 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:23 year:2004 number:5-6 day:22 month:01 pages:318-341 https://doi.org/10.1007/s00170-002-1499-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_21 GBV_ILN_23 GBV_ILN_70 GBV_ILN_150 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2018 GBV_ILN_2241 GBV_ILN_2333 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4307 AR 23 2004 5-6 22 01 318-341 |
| allfields_unstemmed |
10.1007/s00170-002-1499-4 doi (DE-627)OLC2025998805 (DE-He213)s00170-002-1499-4-p DE-627 ger DE-627 rakwb eng 670 VZ Kumar, Rajeev Ranjan verfasserin aut A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model 2004 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2004 Abstract This paper aims to develop a fuzzy-based solution approach to address a machine-loading problem of a flexible manufacturing system (FMS). The proposed solution methodology effectively deals with all the three main constituents of a machine loading problem, viz. job sequence determination, operation machine allocation and the reallocation of jobs. The main objectives of the FMS loading problem considered here are minimisation of system imbalance and maximisation of throughput; the constraints to be satisfied are the available machining time and tool slots. An analytical argument has been provided to support the membership function related to the operation machine allocation vector. Computational results revealed the superiority of the proposed algorithm over other heuristics when it is tested on a standard data set adopted from literature. A new class of petri net model called the “Extended neuro fuzzy petri net” is constructed to capture clearly the various details of the machine loading problem which can be further extended to learn from experience and perform inferences so that truly intelligent system characteristics can be realised. FMS Machine loading Fuzzy logic Petri net Neuro fuzzy Petri net Singh, Amarjit Kumar aut Tiwari, M. K. aut Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 23(2004), 5-6 vom: 22. Jan., Seite 318-341 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:23 year:2004 number:5-6 day:22 month:01 pages:318-341 https://doi.org/10.1007/s00170-002-1499-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_21 GBV_ILN_23 GBV_ILN_70 GBV_ILN_150 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2018 GBV_ILN_2241 GBV_ILN_2333 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4307 AR 23 2004 5-6 22 01 318-341 |
| allfieldsGer |
10.1007/s00170-002-1499-4 doi (DE-627)OLC2025998805 (DE-He213)s00170-002-1499-4-p DE-627 ger DE-627 rakwb eng 670 VZ Kumar, Rajeev Ranjan verfasserin aut A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model 2004 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2004 Abstract This paper aims to develop a fuzzy-based solution approach to address a machine-loading problem of a flexible manufacturing system (FMS). The proposed solution methodology effectively deals with all the three main constituents of a machine loading problem, viz. job sequence determination, operation machine allocation and the reallocation of jobs. The main objectives of the FMS loading problem considered here are minimisation of system imbalance and maximisation of throughput; the constraints to be satisfied are the available machining time and tool slots. An analytical argument has been provided to support the membership function related to the operation machine allocation vector. Computational results revealed the superiority of the proposed algorithm over other heuristics when it is tested on a standard data set adopted from literature. A new class of petri net model called the “Extended neuro fuzzy petri net” is constructed to capture clearly the various details of the machine loading problem which can be further extended to learn from experience and perform inferences so that truly intelligent system characteristics can be realised. FMS Machine loading Fuzzy logic Petri net Neuro fuzzy Petri net Singh, Amarjit Kumar aut Tiwari, M. K. aut Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 23(2004), 5-6 vom: 22. Jan., Seite 318-341 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:23 year:2004 number:5-6 day:22 month:01 pages:318-341 https://doi.org/10.1007/s00170-002-1499-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_21 GBV_ILN_23 GBV_ILN_70 GBV_ILN_150 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2018 GBV_ILN_2241 GBV_ILN_2333 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4307 AR 23 2004 5-6 22 01 318-341 |
| allfieldsSound |
10.1007/s00170-002-1499-4 doi (DE-627)OLC2025998805 (DE-He213)s00170-002-1499-4-p DE-627 ger DE-627 rakwb eng 670 VZ Kumar, Rajeev Ranjan verfasserin aut A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model 2004 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2004 Abstract This paper aims to develop a fuzzy-based solution approach to address a machine-loading problem of a flexible manufacturing system (FMS). The proposed solution methodology effectively deals with all the three main constituents of a machine loading problem, viz. job sequence determination, operation machine allocation and the reallocation of jobs. The main objectives of the FMS loading problem considered here are minimisation of system imbalance and maximisation of throughput; the constraints to be satisfied are the available machining time and tool slots. An analytical argument has been provided to support the membership function related to the operation machine allocation vector. Computational results revealed the superiority of the proposed algorithm over other heuristics when it is tested on a standard data set adopted from literature. A new class of petri net model called the “Extended neuro fuzzy petri net” is constructed to capture clearly the various details of the machine loading problem which can be further extended to learn from experience and perform inferences so that truly intelligent system characteristics can be realised. FMS Machine loading Fuzzy logic Petri net Neuro fuzzy Petri net Singh, Amarjit Kumar aut Tiwari, M. K. aut Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 23(2004), 5-6 vom: 22. Jan., Seite 318-341 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:23 year:2004 number:5-6 day:22 month:01 pages:318-341 https://doi.org/10.1007/s00170-002-1499-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_21 GBV_ILN_23 GBV_ILN_70 GBV_ILN_150 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2018 GBV_ILN_2241 GBV_ILN_2333 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4307 AR 23 2004 5-6 22 01 318-341 |
| language |
English |
| source |
Enthalten in The international journal of advanced manufacturing technology 23(2004), 5-6 vom: 22. Jan., Seite 318-341 volume:23 year:2004 number:5-6 day:22 month:01 pages:318-341 |
| sourceStr |
Enthalten in The international journal of advanced manufacturing technology 23(2004), 5-6 vom: 22. Jan., Seite 318-341 volume:23 year:2004 number:5-6 day:22 month:01 pages:318-341 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
FMS Machine loading Fuzzy logic Petri net Neuro fuzzy Petri net |
| dewey-raw |
670 |
| isfreeaccess_bool |
false |
| container_title |
The international journal of advanced manufacturing technology |
| authorswithroles_txt_mv |
Kumar, Rajeev Ranjan @@aut@@ Singh, Amarjit Kumar @@aut@@ Tiwari, M. K. @@aut@@ |
| publishDateDaySort_date |
2004-01-22T00:00:00Z |
| hierarchy_top_id |
129185299 |
| dewey-sort |
3670 |
| id |
OLC2025998805 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2025998805</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230401073409.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2004 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00170-002-1499-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2025998805</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00170-002-1499-4-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">670</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kumar, Rajeev Ranjan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2004</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London Limited 2004</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper aims to develop a fuzzy-based solution approach to address a machine-loading problem of a flexible manufacturing system (FMS). The proposed solution methodology effectively deals with all the three main constituents of a machine loading problem, viz. job sequence determination, operation machine allocation and the reallocation of jobs. The main objectives of the FMS loading problem considered here are minimisation of system imbalance and maximisation of throughput; the constraints to be satisfied are the available machining time and tool slots. An analytical argument has been provided to support the membership function related to the operation machine allocation vector. Computational results revealed the superiority of the proposed algorithm over other heuristics when it is tested on a standard data set adopted from literature. A new class of petri net model called the “Extended neuro fuzzy petri net” is constructed to capture clearly the various details of the machine loading problem which can be further extended to learn from experience and perform inferences so that truly intelligent system characteristics can be realised.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">FMS</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine loading</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy logic</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Petri net</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neuro fuzzy Petri net</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Singh, Amarjit Kumar</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tiwari, M. K.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">The international journal of advanced manufacturing technology</subfield><subfield code="d">Springer-Verlag, 1985</subfield><subfield code="g">23(2004), 5-6 vom: 22. Jan., Seite 318-341</subfield><subfield code="w">(DE-627)129185299</subfield><subfield code="w">(DE-600)52651-4</subfield><subfield code="w">(DE-576)014456192</subfield><subfield code="x">0268-3768</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2004</subfield><subfield code="g">number:5-6</subfield><subfield code="g">day:22</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:318-341</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00170-002-1499-4</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2241</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">23</subfield><subfield code="j">2004</subfield><subfield code="e">5-6</subfield><subfield code="b">22</subfield><subfield code="c">01</subfield><subfield code="h">318-341</subfield></datafield></record></collection>
|
| author |
Kumar, Rajeev Ranjan |
| spellingShingle |
Kumar, Rajeev Ranjan ddc 670 misc FMS misc Machine loading misc Fuzzy logic misc Petri net misc Neuro fuzzy Petri net A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model |
| authorStr |
Kumar, Rajeev Ranjan |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129185299 |
| format |
Article |
| dewey-ones |
670 - Manufacturing |
| delete_txt_mv |
keep |
| author_role |
aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0268-3768 |
| topic_title |
670 VZ A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model FMS Machine loading Fuzzy logic Petri net Neuro fuzzy Petri net |
| topic |
ddc 670 misc FMS misc Machine loading misc Fuzzy logic misc Petri net misc Neuro fuzzy Petri net |
| topic_unstemmed |
ddc 670 misc FMS misc Machine loading misc Fuzzy logic misc Petri net misc Neuro fuzzy Petri net |
| topic_browse |
ddc 670 misc FMS misc Machine loading misc Fuzzy logic misc Petri net misc Neuro fuzzy Petri net |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
The international journal of advanced manufacturing technology |
| hierarchy_parent_id |
129185299 |
| dewey-tens |
670 - Manufacturing |
| hierarchy_top_title |
The international journal of advanced manufacturing technology |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 |
| title |
A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model |
| ctrlnum |
(DE-627)OLC2025998805 (DE-He213)s00170-002-1499-4-p |
| title_full |
A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model |
| author_sort |
Kumar, Rajeev Ranjan |
| journal |
The international journal of advanced manufacturing technology |
| journalStr |
The international journal of advanced manufacturing technology |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2004 |
| contenttype_str_mv |
txt |
| container_start_page |
318 |
| author_browse |
Kumar, Rajeev Ranjan Singh, Amarjit Kumar Tiwari, M. K. |
| container_volume |
23 |
| class |
670 VZ |
| format_se |
Aufsätze |
| author-letter |
Kumar, Rajeev Ranjan |
| doi_str_mv |
10.1007/s00170-002-1499-4 |
| dewey-full |
670 |
| title_sort |
a fuzzy based algorithm to solve the machine-loading problems of a fms and its neuro fuzzy petri net model |
| title_auth |
A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model |
| abstract |
Abstract This paper aims to develop a fuzzy-based solution approach to address a machine-loading problem of a flexible manufacturing system (FMS). The proposed solution methodology effectively deals with all the three main constituents of a machine loading problem, viz. job sequence determination, operation machine allocation and the reallocation of jobs. The main objectives of the FMS loading problem considered here are minimisation of system imbalance and maximisation of throughput; the constraints to be satisfied are the available machining time and tool slots. An analytical argument has been provided to support the membership function related to the operation machine allocation vector. Computational results revealed the superiority of the proposed algorithm over other heuristics when it is tested on a standard data set adopted from literature. A new class of petri net model called the “Extended neuro fuzzy petri net” is constructed to capture clearly the various details of the machine loading problem which can be further extended to learn from experience and perform inferences so that truly intelligent system characteristics can be realised. © Springer-Verlag London Limited 2004 |
| abstractGer |
Abstract This paper aims to develop a fuzzy-based solution approach to address a machine-loading problem of a flexible manufacturing system (FMS). The proposed solution methodology effectively deals with all the three main constituents of a machine loading problem, viz. job sequence determination, operation machine allocation and the reallocation of jobs. The main objectives of the FMS loading problem considered here are minimisation of system imbalance and maximisation of throughput; the constraints to be satisfied are the available machining time and tool slots. An analytical argument has been provided to support the membership function related to the operation machine allocation vector. Computational results revealed the superiority of the proposed algorithm over other heuristics when it is tested on a standard data set adopted from literature. A new class of petri net model called the “Extended neuro fuzzy petri net” is constructed to capture clearly the various details of the machine loading problem which can be further extended to learn from experience and perform inferences so that truly intelligent system characteristics can be realised. © Springer-Verlag London Limited 2004 |
| abstract_unstemmed |
Abstract This paper aims to develop a fuzzy-based solution approach to address a machine-loading problem of a flexible manufacturing system (FMS). The proposed solution methodology effectively deals with all the three main constituents of a machine loading problem, viz. job sequence determination, operation machine allocation and the reallocation of jobs. The main objectives of the FMS loading problem considered here are minimisation of system imbalance and maximisation of throughput; the constraints to be satisfied are the available machining time and tool slots. An analytical argument has been provided to support the membership function related to the operation machine allocation vector. Computational results revealed the superiority of the proposed algorithm over other heuristics when it is tested on a standard data set adopted from literature. A new class of petri net model called the “Extended neuro fuzzy petri net” is constructed to capture clearly the various details of the machine loading problem which can be further extended to learn from experience and perform inferences so that truly intelligent system characteristics can be realised. © Springer-Verlag London Limited 2004 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_21 GBV_ILN_23 GBV_ILN_70 GBV_ILN_150 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2018 GBV_ILN_2241 GBV_ILN_2333 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4307 |
| container_issue |
5-6 |
| title_short |
A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model |
| url |
https://doi.org/10.1007/s00170-002-1499-4 |
| remote_bool |
false |
| author2 |
Singh, Amarjit Kumar Tiwari, M. K. |
| author2Str |
Singh, Amarjit Kumar Tiwari, M. K. |
| ppnlink |
129185299 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00170-002-1499-4 |
| up_date |
2024-07-04T02:52:01.373Z |
| _version_ |
1803615229836263424 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2025998805</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230401073409.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2004 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00170-002-1499-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2025998805</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00170-002-1499-4-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">670</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kumar, Rajeev Ranjan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A fuzzy based algorithm to solve the machine-loading problems of a FMS and its neuro fuzzy petri net model</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2004</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London Limited 2004</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper aims to develop a fuzzy-based solution approach to address a machine-loading problem of a flexible manufacturing system (FMS). The proposed solution methodology effectively deals with all the three main constituents of a machine loading problem, viz. job sequence determination, operation machine allocation and the reallocation of jobs. The main objectives of the FMS loading problem considered here are minimisation of system imbalance and maximisation of throughput; the constraints to be satisfied are the available machining time and tool slots. An analytical argument has been provided to support the membership function related to the operation machine allocation vector. Computational results revealed the superiority of the proposed algorithm over other heuristics when it is tested on a standard data set adopted from literature. A new class of petri net model called the “Extended neuro fuzzy petri net” is constructed to capture clearly the various details of the machine loading problem which can be further extended to learn from experience and perform inferences so that truly intelligent system characteristics can be realised.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">FMS</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine loading</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy logic</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Petri net</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neuro fuzzy Petri net</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Singh, Amarjit Kumar</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tiwari, M. K.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">The international journal of advanced manufacturing technology</subfield><subfield code="d">Springer-Verlag, 1985</subfield><subfield code="g">23(2004), 5-6 vom: 22. Jan., Seite 318-341</subfield><subfield code="w">(DE-627)129185299</subfield><subfield code="w">(DE-600)52651-4</subfield><subfield code="w">(DE-576)014456192</subfield><subfield code="x">0268-3768</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2004</subfield><subfield code="g">number:5-6</subfield><subfield code="g">day:22</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:318-341</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00170-002-1499-4</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2241</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">23</subfield><subfield code="j">2004</subfield><subfield code="e">5-6</subfield><subfield code="b">22</subfield><subfield code="c">01</subfield><subfield code="h">318-341</subfield></datafield></record></collection>
|
| score |
7.4017916 |