Remanufacturability Evaluation Method for Used Vehicles Based on Stacking Ensemble Learning Framework
This paper proposes a novel method for evaluating the remanufacturability of used vehicles based on Stacking-Based Ensemble Learning Algorithm (SBELA). A method that combines a supply chain evolutionary game model is proposed to construct a Hybrid Dataset (HD), which aims to deal with Remanufacturin...
Gespeichert in:
| Veröffentlicht in: | IEEE access 2023, Vol.11, p.135922-135933 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 135933 |
|---|---|
| container_issue | |
| container_start_page | 135922 |
| container_title | IEEE access |
| container_volume | 11 |
| creator | Wang, Qiucheng Sun, Weice Liu, Zhengqing |
| description | This paper proposes a novel method for evaluating the remanufacturability of used vehicles based on Stacking-Based Ensemble Learning Algorithm (SBELA). A method that combines a supply chain evolutionary game model is proposed to construct a Hybrid Dataset (HD), which aims to deal with Remanufacturing Data Gap (RDG). A modified SBELA is used for evaluating the remanufacturability of used vehicles. The results of this investigation show that HD can be used to evaluate remanufacturability in the initial stage of remanufacturing. The modified SBELA significantly improves the evaluation of remanufacturability performance, compared to the Ridge Regression, Lasso Regression, Linear Regression, Stochastic Gradient Descent (SGD), Kneighbors, AdaBoost, and Gradient Boosting Regression (GBR), the Mean Square Error (MSE) has decreased by 51.88%, 53.75%, 52.05%, 58.15%, 8.33%, 57.92%, and 22.22%, respectively. The investigation results demonstrate HD's effectiveness in evaluating remanufacturability during the initial remanufacturing stage. This can provide a reference for newly established remanufacturing enterprises in the RDG dilemma. |
| doi_str_mv | 10.1109/ACCESS.2023.3334168 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_proquest_journals_2899467214</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10320324</ieee_id><doaj_id>oai_doaj_org_article_fff0ab67a7074913a1ae3f709c7018dd</doaj_id><sourcerecordid>2899467214</sourcerecordid><originalsourceid>FETCH-LOGICAL-c359t-3d9ea46334cff32c853722aad2e131a47ecd7a876fd951b9d6fc272e9d2ef86b3</originalsourceid><addsrcrecordid>eNpNUdFq3DAQNKWBhiRf0D4Y-nxXSWtL1mN6XNrAhUKu6atYS6vEF5-VSnZK_r5yHUoWwa6GmZGWKYqPnK05Z_rL5Waz3e_XgglYA0DFZfOuOBVc6hXUIN-_mT8UFykdWK4mQ7U6LeiWjjhMHu04RWy7vhtfyu0z9hOOXRjKGxofgit9iOVdIlf-oofO9pTKrzhfM2M_on3shvtyOyQ6tj2VO8I4zMhVxCP9CfHxvDjx2Ce6eO1nxd3V9ufm-2r349v15nK3slDrcQVOE1Yy72C9B2GbGpQQiE4QB46VIusUNkp6p2veaie9FUqQzgTfyBbOiuvF1wU8mKfYHTG-mICd-QeEeG8wjvMCxnvPsJUKFVOV5oAcCbxi2irGG-ey1-fF6ymG3xOl0RzCFIf8fSMarSupBK8yCxaWjSGlSP7_q5yZOR6zxGPmeMxrPFn1aVF1RPRGASKfCv4C822Mvw</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2899467214</pqid></control><display><type>article</type><title>Remanufacturability Evaluation Method for Used Vehicles Based on Stacking Ensemble Learning Framework</title><source>IEEE Open Access Journals</source><source>DOAJ Directory of Open Access Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Wang, Qiucheng ; Sun, Weice ; Liu, Zhengqing</creator><creatorcontrib>Wang, Qiucheng ; Sun, Weice ; Liu, Zhengqing</creatorcontrib><description>This paper proposes a novel method for evaluating the remanufacturability of used vehicles based on Stacking-Based Ensemble Learning Algorithm (SBELA). A method that combines a supply chain evolutionary game model is proposed to construct a Hybrid Dataset (HD), which aims to deal with Remanufacturing Data Gap (RDG). A modified SBELA is used for evaluating the remanufacturability of used vehicles. The results of this investigation show that HD can be used to evaluate remanufacturability in the initial stage of remanufacturing. The modified SBELA significantly improves the evaluation of remanufacturability performance, compared to the Ridge Regression, Lasso Regression, Linear Regression, Stochastic Gradient Descent (SGD), Kneighbors, AdaBoost, and Gradient Boosting Regression (GBR), the Mean Square Error (MSE) has decreased by 51.88%, 53.75%, 52.05%, 58.15%, 8.33%, 57.92%, and 22.22%, respectively. The investigation results demonstrate HD's effectiveness in evaluating remanufacturability during the initial remanufacturing stage. This can provide a reference for newly established remanufacturing enterprises in the RDG dilemma.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2023.3334168</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Costs ; Data models ; Ensemble learning ; evaluation of remanufacturability ; Games ; hybrid dataset ; Machine learning ; Manufacturing processes ; Performance evaluation ; Predictive models ; Production management ; Regression ; Remanufacturing ; remanufacturing data gap ; Solid modeling ; Stacking ; Stacking ensemble learning ; Supply chains ; Used automobiles ; Vehicles</subject><ispartof>IEEE access, 2023, Vol.11, p.135922-135933</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c359t-3d9ea46334cff32c853722aad2e131a47ecd7a876fd951b9d6fc272e9d2ef86b3</cites><orcidid>0009-0006-9165-2355 ; 0000-0002-9553-1554 ; 0000-0002-3264-4893</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10320324$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2100,4022,27632,27922,27923,27924,54932</link.rule.ids></links><search><creatorcontrib>Wang, Qiucheng</creatorcontrib><creatorcontrib>Sun, Weice</creatorcontrib><creatorcontrib>Liu, Zhengqing</creatorcontrib><title>Remanufacturability Evaluation Method for Used Vehicles Based on Stacking Ensemble Learning Framework</title><title>IEEE access</title><addtitle>Access</addtitle><description>This paper proposes a novel method for evaluating the remanufacturability of used vehicles based on Stacking-Based Ensemble Learning Algorithm (SBELA). A method that combines a supply chain evolutionary game model is proposed to construct a Hybrid Dataset (HD), which aims to deal with Remanufacturing Data Gap (RDG). A modified SBELA is used for evaluating the remanufacturability of used vehicles. The results of this investigation show that HD can be used to evaluate remanufacturability in the initial stage of remanufacturing. The modified SBELA significantly improves the evaluation of remanufacturability performance, compared to the Ridge Regression, Lasso Regression, Linear Regression, Stochastic Gradient Descent (SGD), Kneighbors, AdaBoost, and Gradient Boosting Regression (GBR), the Mean Square Error (MSE) has decreased by 51.88%, 53.75%, 52.05%, 58.15%, 8.33%, 57.92%, and 22.22%, respectively. The investigation results demonstrate HD's effectiveness in evaluating remanufacturability during the initial remanufacturing stage. This can provide a reference for newly established remanufacturing enterprises in the RDG dilemma.</description><subject>Algorithms</subject><subject>Costs</subject><subject>Data models</subject><subject>Ensemble learning</subject><subject>evaluation of remanufacturability</subject><subject>Games</subject><subject>hybrid dataset</subject><subject>Machine learning</subject><subject>Manufacturing processes</subject><subject>Performance evaluation</subject><subject>Predictive models</subject><subject>Production management</subject><subject>Regression</subject><subject>Remanufacturing</subject><subject>remanufacturing data gap</subject><subject>Solid modeling</subject><subject>Stacking</subject><subject>Stacking ensemble learning</subject><subject>Supply chains</subject><subject>Used automobiles</subject><subject>Vehicles</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUdFq3DAQNKWBhiRf0D4Y-nxXSWtL1mN6XNrAhUKu6atYS6vEF5-VSnZK_r5yHUoWwa6GmZGWKYqPnK05Z_rL5Waz3e_XgglYA0DFZfOuOBVc6hXUIN-_mT8UFykdWK4mQ7U6LeiWjjhMHu04RWy7vhtfyu0z9hOOXRjKGxofgit9iOVdIlf-oofO9pTKrzhfM2M_on3shvtyOyQ6tj2VO8I4zMhVxCP9CfHxvDjx2Ce6eO1nxd3V9ufm-2r349v15nK3slDrcQVOE1Yy72C9B2GbGpQQiE4QB46VIusUNkp6p2veaie9FUqQzgTfyBbOiuvF1wU8mKfYHTG-mICd-QeEeG8wjvMCxnvPsJUKFVOV5oAcCbxi2irGG-ey1-fF6ymG3xOl0RzCFIf8fSMarSupBK8yCxaWjSGlSP7_q5yZOR6zxGPmeMxrPFn1aVF1RPRGASKfCv4C822Mvw</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Wang, Qiucheng</creator><creator>Sun, Weice</creator><creator>Liu, Zhengqing</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0009-0006-9165-2355</orcidid><orcidid>https://orcid.org/0000-0002-9553-1554</orcidid><orcidid>https://orcid.org/0000-0002-3264-4893</orcidid></search><sort><creationdate>2023</creationdate><title>Remanufacturability Evaluation Method for Used Vehicles Based on Stacking Ensemble Learning Framework</title><author>Wang, Qiucheng ; Sun, Weice ; Liu, Zhengqing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-3d9ea46334cff32c853722aad2e131a47ecd7a876fd951b9d6fc272e9d2ef86b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Costs</topic><topic>Data models</topic><topic>Ensemble learning</topic><topic>evaluation of remanufacturability</topic><topic>Games</topic><topic>hybrid dataset</topic><topic>Machine learning</topic><topic>Manufacturing processes</topic><topic>Performance evaluation</topic><topic>Predictive models</topic><topic>Production management</topic><topic>Regression</topic><topic>Remanufacturing</topic><topic>remanufacturing data gap</topic><topic>Solid modeling</topic><topic>Stacking</topic><topic>Stacking ensemble learning</topic><topic>Supply chains</topic><topic>Used automobiles</topic><topic>Vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Qiucheng</creatorcontrib><creatorcontrib>Sun, Weice</creatorcontrib><creatorcontrib>Liu, Zhengqing</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Qiucheng</au><au>Sun, Weice</au><au>Liu, Zhengqing</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Remanufacturability Evaluation Method for Used Vehicles Based on Stacking Ensemble Learning Framework</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2023</date><risdate>2023</risdate><volume>11</volume><spage>135922</spage><epage>135933</epage><pages>135922-135933</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>This paper proposes a novel method for evaluating the remanufacturability of used vehicles based on Stacking-Based Ensemble Learning Algorithm (SBELA). A method that combines a supply chain evolutionary game model is proposed to construct a Hybrid Dataset (HD), which aims to deal with Remanufacturing Data Gap (RDG). A modified SBELA is used for evaluating the remanufacturability of used vehicles. The results of this investigation show that HD can be used to evaluate remanufacturability in the initial stage of remanufacturing. The modified SBELA significantly improves the evaluation of remanufacturability performance, compared to the Ridge Regression, Lasso Regression, Linear Regression, Stochastic Gradient Descent (SGD), Kneighbors, AdaBoost, and Gradient Boosting Regression (GBR), the Mean Square Error (MSE) has decreased by 51.88%, 53.75%, 52.05%, 58.15%, 8.33%, 57.92%, and 22.22%, respectively. The investigation results demonstrate HD's effectiveness in evaluating remanufacturability during the initial remanufacturing stage. This can provide a reference for newly established remanufacturing enterprises in the RDG dilemma.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2023.3334168</doi><tpages>12</tpages><orcidid>https://orcid.org/0009-0006-9165-2355</orcidid><orcidid>https://orcid.org/0000-0002-9553-1554</orcidid><orcidid>https://orcid.org/0000-0002-3264-4893</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2169-3536 |
| ispartof | IEEE access, 2023, Vol.11, p.135922-135933 |
| issn | 2169-3536 2169-3536 |
| language | eng |
| recordid | cdi_proquest_journals_2899467214 |
| source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | Algorithms Costs Data models Ensemble learning evaluation of remanufacturability Games hybrid dataset Machine learning Manufacturing processes Performance evaluation Predictive models Production management Regression Remanufacturing remanufacturing data gap Solid modeling Stacking Stacking ensemble learning Supply chains Used automobiles Vehicles |
| title | Remanufacturability Evaluation Method for Used Vehicles Based on Stacking Ensemble Learning Framework |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-10T11%3A23%3A58IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Remanufacturability%20Evaluation%20Method%20for%20Used%20Vehicles%20Based%20on%20Stacking%20Ensemble%20Learning%20Framework&rft.jtitle=IEEE%20access&rft.au=Wang,%20Qiucheng&rft.date=2023&rft.volume=11&rft.spage=135922&rft.epage=135933&rft.pages=135922-135933&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2023.3334168&rft_dat=%3Cproquest_ieee_%3E2899467214%3C/proquest_ieee_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2899467214&rft_id=info:pmid/&rft_ieee_id=10320324&rft_doaj_id=oai_doaj_org_article_fff0ab67a7074913a1ae3f709c7018dd&rfr_iscdi=true |