A Self-Learning Discrete Jaya Algorithm for Multiobjective Energy-Efficient Distributed No-Idle Flow-Shop Scheduling Problem in Heterogeneous Factory System
In this study, a self-learning discrete Jaya algorithm (SD-Jaya) is proposed to address the energy-efficient distributed no-idle flow-shop scheduling problem (FSP) in a heterogeneous factory system (HFS-EEDNIFSP) with the criteria of minimizing the total tardiness (TTD), total energy consumption (TE...
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| Veröffentlicht in: | IEEE transactions on cybernetics 2022-12, Vol.52 (12), p.12675-12686 |
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| creator | Zhao, Fuqing Ma, Ru Wang, Ling |
| description | In this study, a self-learning discrete Jaya algorithm (SD-Jaya) is proposed to address the energy-efficient distributed no-idle flow-shop scheduling problem (FSP) in a heterogeneous factory system (HFS-EEDNIFSP) with the criteria of minimizing the total tardiness (TTD), total energy consumption (TEC), and factory load balancing (FLB). First, the mixed-integer programming model of HFS-EEDNIFSP is presented. An evaluation criterion of FLB combining the energy consumption and the completion time is introduced. Second, a self-learning operators selection strategy, in which the success rate of each operator is summarized as knowledge, is designed for guiding the selection of operators. Third, the energy-saving strategy is proposed for reducing the TEC. The energy-efficient no-idle FSP is transformed to be an energy-efficient permutation FSP to search the idle times. The speed of operations which adjacent are idle times is reduced. The effectiveness of SD-Jaya is tested on 60 benchmark instances. On the quality of the solution, the experimental results reveal that the efficacy of the SD-Jaya algorithm outperforms the other algorithms for addressing HFS-EEDNIFSP. |
| doi_str_mv | 10.1109/TCYB.2021.3086181 |
| format | Article |
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First, the mixed-integer programming model of HFS-EEDNIFSP is presented. An evaluation criterion of FLB combining the energy consumption and the completion time is introduced. Second, a self-learning operators selection strategy, in which the success rate of each operator is summarized as knowledge, is designed for guiding the selection of operators. Third, the energy-saving strategy is proposed for reducing the TEC. The energy-efficient no-idle FSP is transformed to be an energy-efficient permutation FSP to search the idle times. The speed of operations which adjacent are idle times is reduced. The effectiveness of SD-Jaya is tested on 60 benchmark instances. On the quality of the solution, the experimental results reveal that the efficacy of the SD-Jaya algorithm outperforms the other algorithms for addressing HFS-EEDNIFSP.</description><identifier>ISSN: 2168-2267</identifier><identifier>EISSN: 2168-2275</identifier><identifier>DOI: 10.1109/TCYB.2021.3086181</identifier><identifier>PMID: 34415843</identifier><identifier>CODEN: ITCEB8</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Completion time ; Energy consumption ; Energy-efficient distributed no-idle flow-shop scheduling problem (FSP) ; energy-saving strategy ; heterogeneous factory system ; Idling ; Integer programming ; Jaya algorithm ; Job shop scheduling ; Machine learning ; Mixed integer ; Operators ; Optimization ; Permutations ; self-learning operation selection strategy (SLOS)</subject><ispartof>IEEE transactions on cybernetics, 2022-12, Vol.52 (12), p.12675-12686</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c374t-aaed54af4194acbdb2db437ffed8ad21a7d72f488976d78856de0fe2b1df6c7c3</citedby><cites>FETCH-LOGICAL-c374t-aaed54af4194acbdb2db437ffed8ad21a7d72f488976d78856de0fe2b1df6c7c3</cites><orcidid>0000-0002-7336-9699 ; 0000-0001-8964-6454</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9520219$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9520219$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhao, Fuqing</creatorcontrib><creatorcontrib>Ma, Ru</creatorcontrib><creatorcontrib>Wang, Ling</creatorcontrib><title>A Self-Learning Discrete Jaya Algorithm for Multiobjective Energy-Efficient Distributed No-Idle Flow-Shop Scheduling Problem in Heterogeneous Factory System</title><title>IEEE transactions on cybernetics</title><addtitle>TCYB</addtitle><description>In this study, a self-learning discrete Jaya algorithm (SD-Jaya) is proposed to address the energy-efficient distributed no-idle flow-shop scheduling problem (FSP) in a heterogeneous factory system (HFS-EEDNIFSP) with the criteria of minimizing the total tardiness (TTD), total energy consumption (TEC), and factory load balancing (FLB). First, the mixed-integer programming model of HFS-EEDNIFSP is presented. An evaluation criterion of FLB combining the energy consumption and the completion time is introduced. Second, a self-learning operators selection strategy, in which the success rate of each operator is summarized as knowledge, is designed for guiding the selection of operators. Third, the energy-saving strategy is proposed for reducing the TEC. The energy-efficient no-idle FSP is transformed to be an energy-efficient permutation FSP to search the idle times. The speed of operations which adjacent are idle times is reduced. The effectiveness of SD-Jaya is tested on 60 benchmark instances. On the quality of the solution, the experimental results reveal that the efficacy of the SD-Jaya algorithm outperforms the other algorithms for addressing HFS-EEDNIFSP.</description><subject>Algorithms</subject><subject>Completion time</subject><subject>Energy consumption</subject><subject>Energy-efficient distributed no-idle flow-shop scheduling problem (FSP)</subject><subject>energy-saving strategy</subject><subject>heterogeneous factory system</subject><subject>Idling</subject><subject>Integer programming</subject><subject>Jaya algorithm</subject><subject>Job shop scheduling</subject><subject>Machine learning</subject><subject>Mixed integer</subject><subject>Operators</subject><subject>Optimization</subject><subject>Permutations</subject><subject>self-learning operation selection strategy (SLOS)</subject><issn>2168-2267</issn><issn>2168-2275</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkU1v2zAMho1hw1p0_QHDLgJ22cWZ9WFLPmZZsnbIPoB0h50MWaISBbKVSvIG_5f-2NpL0cN4IUE8L0nwzbK3uFhgXNQf71a_Py1IQfCCFqLCAr_ILgmuRE4IL18-1xW_yK5jPBZTiKlVi9fZBWUMl4LRy-xhiXbgTL4FGXrb79FnG1WABOirHCVaur0PNh06ZHxA3waXrG-PoJL9A2jdQ9iP-doYqyz0adamYNshgUbffX6rHaCN83_z3cGf0E4dQA9uXvIz-NZBh2yPbqZdwe-hBz9EtJEq-TCi3RgTdG-yV0a6CNdP-Sr7tVnfrW7y7Y8vt6vlNleUs5RLCbpk0jBcM6la3RLdMsqNAS2kJlhyzYlhQtS80lyIstJQGCAt1qZSXNGr7MN57in4-wFiarrpC-Cc_HdVQ8qKMlJSWk7o-__Qox9CP13XEE55WVU1oROFz5QKPsYApjkF28kwNrhoZvea2b1mdq95cm_SvDtrLAA883U5QzV9BN4dluM</recordid><startdate>20221201</startdate><enddate>20221201</enddate><creator>Zhao, Fuqing</creator><creator>Ma, Ru</creator><creator>Wang, Ling</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-7336-9699</orcidid><orcidid>https://orcid.org/0000-0001-8964-6454</orcidid></search><sort><creationdate>20221201</creationdate><title>A Self-Learning Discrete Jaya Algorithm for Multiobjective Energy-Efficient Distributed No-Idle Flow-Shop Scheduling Problem in Heterogeneous Factory System</title><author>Zhao, Fuqing ; Ma, Ru ; Wang, Ling</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c374t-aaed54af4194acbdb2db437ffed8ad21a7d72f488976d78856de0fe2b1df6c7c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Completion time</topic><topic>Energy consumption</topic><topic>Energy-efficient distributed no-idle flow-shop scheduling problem (FSP)</topic><topic>energy-saving strategy</topic><topic>heterogeneous factory system</topic><topic>Idling</topic><topic>Integer programming</topic><topic>Jaya algorithm</topic><topic>Job shop scheduling</topic><topic>Machine learning</topic><topic>Mixed integer</topic><topic>Operators</topic><topic>Optimization</topic><topic>Permutations</topic><topic>self-learning operation selection strategy (SLOS)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Fuqing</creatorcontrib><creatorcontrib>Ma, Ru</creatorcontrib><creatorcontrib>Wang, Ling</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</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>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace 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>MEDLINE - Academic</collection><jtitle>IEEE transactions on cybernetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhao, Fuqing</au><au>Ma, Ru</au><au>Wang, Ling</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Self-Learning Discrete Jaya Algorithm for Multiobjective Energy-Efficient Distributed No-Idle Flow-Shop Scheduling Problem in Heterogeneous Factory System</atitle><jtitle>IEEE transactions on cybernetics</jtitle><stitle>TCYB</stitle><date>2022-12-01</date><risdate>2022</risdate><volume>52</volume><issue>12</issue><spage>12675</spage><epage>12686</epage><pages>12675-12686</pages><issn>2168-2267</issn><eissn>2168-2275</eissn><coden>ITCEB8</coden><abstract>In this study, a self-learning discrete Jaya algorithm (SD-Jaya) is proposed to address the energy-efficient distributed no-idle flow-shop scheduling problem (FSP) in a heterogeneous factory system (HFS-EEDNIFSP) with the criteria of minimizing the total tardiness (TTD), total energy consumption (TEC), and factory load balancing (FLB). First, the mixed-integer programming model of HFS-EEDNIFSP is presented. An evaluation criterion of FLB combining the energy consumption and the completion time is introduced. Second, a self-learning operators selection strategy, in which the success rate of each operator is summarized as knowledge, is designed for guiding the selection of operators. Third, the energy-saving strategy is proposed for reducing the TEC. The energy-efficient no-idle FSP is transformed to be an energy-efficient permutation FSP to search the idle times. The speed of operations which adjacent are idle times is reduced. The effectiveness of SD-Jaya is tested on 60 benchmark instances. On the quality of the solution, the experimental results reveal that the efficacy of the SD-Jaya algorithm outperforms the other algorithms for addressing HFS-EEDNIFSP.</abstract><cop>Piscataway</cop><pub>IEEE</pub><pmid>34415843</pmid><doi>10.1109/TCYB.2021.3086181</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-7336-9699</orcidid><orcidid>https://orcid.org/0000-0001-8964-6454</orcidid></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Completion time Energy consumption Energy-efficient distributed no-idle flow-shop scheduling problem (FSP) energy-saving strategy heterogeneous factory system Idling Integer programming Jaya algorithm Job shop scheduling Machine learning Mixed integer Operators Optimization Permutations self-learning operation selection strategy (SLOS) |
| title | A Self-Learning Discrete Jaya Algorithm for Multiobjective Energy-Efficient Distributed No-Idle Flow-Shop Scheduling Problem in Heterogeneous Factory System |
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