Fractional-order optimal control model for the equipment management optimization problem with preventive maintenance
Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to ch...
Ausführliche Beschreibung
Autor*in: |
Gong, Yanping [verfasserIn] |
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Englisch |
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2021 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
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Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 34(2021), 6 vom: 08. Nov., Seite 4693-4714 |
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Übergeordnetes Werk: |
volume:34 ; year:2021 ; number:6 ; day:08 ; month:11 ; pages:4693-4714 |
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DOI / URN: |
10.1007/s00521-021-06624-0 |
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Katalog-ID: |
OLC2078165573 |
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520 | |a Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect. | ||
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10.1007/s00521-021-06624-0 doi (DE-627)OLC2078165573 (DE-He213)s00521-021-06624-0-p DE-627 ger DE-627 rakwb eng 004 VZ Gong, Yanping verfasserin aut Fractional-order optimal control model for the equipment management optimization problem with preventive maintenance 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect. Equipment management Memory effect Fractional-order model Optimal control Equipment quality function Zha, Mingjiang aut Lv, Zhanmei (orcid)0000-0002-0524-9513 aut Enthalten in Neural computing & applications Springer London, 1993 34(2021), 6 vom: 08. Nov., Seite 4693-4714 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2021 number:6 day:08 month:11 pages:4693-4714 https://doi.org/10.1007/s00521-021-06624-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2021 6 08 11 4693-4714 |
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10.1007/s00521-021-06624-0 doi (DE-627)OLC2078165573 (DE-He213)s00521-021-06624-0-p DE-627 ger DE-627 rakwb eng 004 VZ Gong, Yanping verfasserin aut Fractional-order optimal control model for the equipment management optimization problem with preventive maintenance 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect. Equipment management Memory effect Fractional-order model Optimal control Equipment quality function Zha, Mingjiang aut Lv, Zhanmei (orcid)0000-0002-0524-9513 aut Enthalten in Neural computing & applications Springer London, 1993 34(2021), 6 vom: 08. Nov., Seite 4693-4714 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2021 number:6 day:08 month:11 pages:4693-4714 https://doi.org/10.1007/s00521-021-06624-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2021 6 08 11 4693-4714 |
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10.1007/s00521-021-06624-0 doi (DE-627)OLC2078165573 (DE-He213)s00521-021-06624-0-p DE-627 ger DE-627 rakwb eng 004 VZ Gong, Yanping verfasserin aut Fractional-order optimal control model for the equipment management optimization problem with preventive maintenance 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect. Equipment management Memory effect Fractional-order model Optimal control Equipment quality function Zha, Mingjiang aut Lv, Zhanmei (orcid)0000-0002-0524-9513 aut Enthalten in Neural computing & applications Springer London, 1993 34(2021), 6 vom: 08. Nov., Seite 4693-4714 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2021 number:6 day:08 month:11 pages:4693-4714 https://doi.org/10.1007/s00521-021-06624-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2021 6 08 11 4693-4714 |
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10.1007/s00521-021-06624-0 doi (DE-627)OLC2078165573 (DE-He213)s00521-021-06624-0-p DE-627 ger DE-627 rakwb eng 004 VZ Gong, Yanping verfasserin aut Fractional-order optimal control model for the equipment management optimization problem with preventive maintenance 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect. Equipment management Memory effect Fractional-order model Optimal control Equipment quality function Zha, Mingjiang aut Lv, Zhanmei (orcid)0000-0002-0524-9513 aut Enthalten in Neural computing & applications Springer London, 1993 34(2021), 6 vom: 08. Nov., Seite 4693-4714 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2021 number:6 day:08 month:11 pages:4693-4714 https://doi.org/10.1007/s00521-021-06624-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2021 6 08 11 4693-4714 |
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Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
abstractGer |
Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
abstract_unstemmed |
Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2078165573</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505233116.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">221220s2021 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-021-06624-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2078165573</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00521-021-06624-0-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">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Gong, Yanping</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fractional-order optimal control model for the equipment management optimization problem with preventive maintenance</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</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">© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The current quality status of most machinery and equipment is based on its accumulated historical status, but the influence of the past quality status on the current status of equipment is often overlooked in optimization management. This paper uses a Caputo-type fractional derivative to characterize this property. By refining the nature and characteristics of the equipment maintenance effect function and considering the memory characteristics of equipment quality, the existing model is improved, and a fractional-order optimal control model for equipment maintenance and replacement is constructed. Theoretical analyses verify the effectiveness of the fractional-order equipment maintenance management model. Furthermore, the results of numerical experiments also reflect this difference between integer-order and fractional-order equipment maintenance management models. The result shows that with an increase of the order $$\alpha$$, the optimal target value of the equipment maintenance management problem will also increase with the weakening of the memory effect.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Equipment management</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Memory effect</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fractional-order model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optimal control</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Equipment quality function</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zha, Mingjiang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lv, Zhanmei</subfield><subfield code="0">(orcid)0000-0002-0524-9513</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">Springer London, 1993</subfield><subfield code="g">34(2021), 6 vom: 08. 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