Research on quality traceability of cigarette by combining PDCA quality cycle with information strategy based on fuzzy classification
For the weak part of the quality traceability ability of cigarette logistics and products, this paper proposes a quality traceability method which integrates PDCA quality cycle with information strategy. This method firstly establishes the quality cycle process of cigarette logistics through PDCA qu...
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Veröffentlicht in: | Journal of intelligent & fuzzy systems 2021-01, Vol.40 (4), p.8217-8226 |
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creator | Wang, Xiaodong Wang, Xiaoming Wu, Junfeng Zheng, Kai Pang, Yanhong Gang, Song |
description | For the weak part of the quality traceability ability of cigarette logistics and products, this paper proposes a quality traceability method which integrates PDCA quality cycle with information strategy. This method firstly establishes the quality cycle process of cigarette logistics through PDCA quality cycle process, and then constructs IS-PDCA process based on information strategy (IS) to analyze the quality traceability process of cigarette products. The key links are determined according to the traceability process of cigarette logistics, and the traceability resource scheduling function is determined through the product. Then, according to the determined scheduling function and RFID technology, the optimal allocation strategy is constructed to complete the feature extraction and classification identification of cigarette quality labels. For assessing the quality of cigarette evaluation, classification based on fuzzy is proposed and artificial neural network are utilized for calculating the grade of cigarette. Finally, a process of cigarette quality traceability combining PDCA quality cycle and information strategy is formed, and the quality traceability results are constructed by means of QR code technology, so as to realize the process system of cigarette quality traceability and improve the quality control ability of cigarettes. The simulation results show that the cigarette quality traceability method constructed in this paper can obtain the cigarette quality control with good adaptive performance, and the control process shows a strong ability, which improves the feasibility and effectiveness of the cigarette quality traceability. |
doi_str_mv | 10.3233/JIFS-189644 |
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This method firstly establishes the quality cycle process of cigarette logistics through PDCA quality cycle process, and then constructs IS-PDCA process based on information strategy (IS) to analyze the quality traceability process of cigarette products. The key links are determined according to the traceability process of cigarette logistics, and the traceability resource scheduling function is determined through the product. Then, according to the determined scheduling function and RFID technology, the optimal allocation strategy is constructed to complete the feature extraction and classification identification of cigarette quality labels. For assessing the quality of cigarette evaluation, classification based on fuzzy is proposed and artificial neural network are utilized for calculating the grade of cigarette. Finally, a process of cigarette quality traceability combining PDCA quality cycle and information strategy is formed, and the quality traceability results are constructed by means of QR code technology, so as to realize the process system of cigarette quality traceability and improve the quality control ability of cigarettes. The simulation results show that the cigarette quality traceability method constructed in this paper can obtain the cigarette quality control with good adaptive performance, and the control process shows a strong ability, which improves the feasibility and effectiveness of the cigarette quality traceability.</description><identifier>ISSN: 1064-1246</identifier><identifier>EISSN: 1875-8967</identifier><identifier>DOI: 10.3233/JIFS-189644</identifier><language>eng</language><publisher>Amsterdam: IOS Press BV</publisher><subject>Adaptive control ; Artificial neural networks ; Cigarettes ; Classification ; Feature extraction ; Logistics ; Quality assessment ; Quality control ; Resource scheduling</subject><ispartof>Journal of intelligent & fuzzy systems, 2021-01, Vol.40 (4), p.8217-8226</ispartof><rights>Copyright IOS Press BV 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c261t-d406a257ecd092ea7846e5bf595ab30c41c608b3fd56991978d32c49d6967193</citedby><cites>FETCH-LOGICAL-c261t-d406a257ecd092ea7846e5bf595ab30c41c608b3fd56991978d32c49d6967193</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><contributor>Gonzalez Crespo, Ruben</contributor><contributor>Sanjuán Martínez, Oscar</contributor><contributor>Fenza, Giuseppe</contributor><creatorcontrib>Wang, Xiaodong</creatorcontrib><creatorcontrib>Wang, Xiaoming</creatorcontrib><creatorcontrib>Wu, Junfeng</creatorcontrib><creatorcontrib>Zheng, Kai</creatorcontrib><creatorcontrib>Pang, Yanhong</creatorcontrib><creatorcontrib>Gang, Song</creatorcontrib><title>Research on quality traceability of cigarette by combining PDCA quality cycle with information strategy based on fuzzy classification</title><title>Journal of intelligent & fuzzy systems</title><description>For the weak part of the quality traceability ability of cigarette logistics and products, this paper proposes a quality traceability method which integrates PDCA quality cycle with information strategy. This method firstly establishes the quality cycle process of cigarette logistics through PDCA quality cycle process, and then constructs IS-PDCA process based on information strategy (IS) to analyze the quality traceability process of cigarette products. The key links are determined according to the traceability process of cigarette logistics, and the traceability resource scheduling function is determined through the product. Then, according to the determined scheduling function and RFID technology, the optimal allocation strategy is constructed to complete the feature extraction and classification identification of cigarette quality labels. For assessing the quality of cigarette evaluation, classification based on fuzzy is proposed and artificial neural network are utilized for calculating the grade of cigarette. Finally, a process of cigarette quality traceability combining PDCA quality cycle and information strategy is formed, and the quality traceability results are constructed by means of QR code technology, so as to realize the process system of cigarette quality traceability and improve the quality control ability of cigarettes. The simulation results show that the cigarette quality traceability method constructed in this paper can obtain the cigarette quality control with good adaptive performance, and the control process shows a strong ability, which improves the feasibility and effectiveness of the cigarette quality traceability.</description><subject>Adaptive control</subject><subject>Artificial neural networks</subject><subject>Cigarettes</subject><subject>Classification</subject><subject>Feature extraction</subject><subject>Logistics</subject><subject>Quality assessment</subject><subject>Quality control</subject><subject>Resource scheduling</subject><issn>1064-1246</issn><issn>1875-8967</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNo9kM1KAzEURoMoWKsrXyDgUkbzP5NlqVYrBUW7HzKZpE2ZzrRJikz3vrdpK67u_eDc78IB4BajB0oofXybTr4yXEjB2BkY4CLnWQr5edqRYBkmTFyCqxBWCOGcEzQAP58mGOX1EnYt3O5U42IPo1faqModQ2ehdgvlTYwGVj3U3bpyrWsX8ONpPPq_0b1uDPx2cQldazu_VtGlypC6oln0sFLB1IcndrffJ7xRITjr9BG7BhdWNcHc_M0hmE-e5-PXbPb-Mh2PZpkmAsesZkgownOjaySJUXnBhOGV5ZKriiLNsBaoqKituZASy7yoKdFM1iJJwJIOwd2pduO77c6EWK66nW_Tx5JwnHiGKUrU_YnSvgvBG1tuvFsr35cYlQfN5UFzedJMfwG5h3Hi</recordid><startdate>20210101</startdate><enddate>20210101</enddate><creator>Wang, Xiaodong</creator><creator>Wang, Xiaoming</creator><creator>Wu, Junfeng</creator><creator>Zheng, Kai</creator><creator>Pang, Yanhong</creator><creator>Gang, Song</creator><general>IOS Press BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20210101</creationdate><title>Research on quality traceability of cigarette by combining PDCA quality cycle with information strategy based on fuzzy classification</title><author>Wang, Xiaodong ; Wang, Xiaoming ; Wu, Junfeng ; Zheng, Kai ; Pang, Yanhong ; Gang, Song</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c261t-d406a257ecd092ea7846e5bf595ab30c41c608b3fd56991978d32c49d6967193</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adaptive control</topic><topic>Artificial neural networks</topic><topic>Cigarettes</topic><topic>Classification</topic><topic>Feature extraction</topic><topic>Logistics</topic><topic>Quality assessment</topic><topic>Quality control</topic><topic>Resource scheduling</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Xiaodong</creatorcontrib><creatorcontrib>Wang, Xiaoming</creatorcontrib><creatorcontrib>Wu, Junfeng</creatorcontrib><creatorcontrib>Zheng, Kai</creatorcontrib><creatorcontrib>Pang, Yanhong</creatorcontrib><creatorcontrib>Gang, Song</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology 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><jtitle>Journal of intelligent & fuzzy systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Xiaodong</au><au>Wang, Xiaoming</au><au>Wu, Junfeng</au><au>Zheng, Kai</au><au>Pang, Yanhong</au><au>Gang, Song</au><au>Gonzalez Crespo, Ruben</au><au>Sanjuán Martínez, Oscar</au><au>Fenza, Giuseppe</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Research on quality traceability of cigarette by combining PDCA quality cycle with information strategy based on fuzzy classification</atitle><jtitle>Journal of intelligent & fuzzy systems</jtitle><date>2021-01-01</date><risdate>2021</risdate><volume>40</volume><issue>4</issue><spage>8217</spage><epage>8226</epage><pages>8217-8226</pages><issn>1064-1246</issn><eissn>1875-8967</eissn><abstract>For the weak part of the quality traceability ability of cigarette logistics and products, this paper proposes a quality traceability method which integrates PDCA quality cycle with information strategy. This method firstly establishes the quality cycle process of cigarette logistics through PDCA quality cycle process, and then constructs IS-PDCA process based on information strategy (IS) to analyze the quality traceability process of cigarette products. The key links are determined according to the traceability process of cigarette logistics, and the traceability resource scheduling function is determined through the product. Then, according to the determined scheduling function and RFID technology, the optimal allocation strategy is constructed to complete the feature extraction and classification identification of cigarette quality labels. For assessing the quality of cigarette evaluation, classification based on fuzzy is proposed and artificial neural network are utilized for calculating the grade of cigarette. Finally, a process of cigarette quality traceability combining PDCA quality cycle and information strategy is formed, and the quality traceability results are constructed by means of QR code technology, so as to realize the process system of cigarette quality traceability and improve the quality control ability of cigarettes. The simulation results show that the cigarette quality traceability method constructed in this paper can obtain the cigarette quality control with good adaptive performance, and the control process shows a strong ability, which improves the feasibility and effectiveness of the cigarette quality traceability.</abstract><cop>Amsterdam</cop><pub>IOS Press BV</pub><doi>10.3233/JIFS-189644</doi><tpages>10</tpages></addata></record> |
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subjects | Adaptive control Artificial neural networks Cigarettes Classification Feature extraction Logistics Quality assessment Quality control Resource scheduling |
title | Research on quality traceability of cigarette by combining PDCA quality cycle with information strategy based on fuzzy classification |
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