Differential evolution variants to schedule flexible assembly lines
Scarce resources such as material, labor, and equipment are to be optimized to improve the performance and lower production costs in flexible assembly lines. These resources are usually allocated optimally through the generation of schedules. In this paper, variants of a differential evolution-based...
Gespeichert in:
| Veröffentlicht in: | Journal of intelligent manufacturing 2014-08, Vol.25 (4), p.739-753 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 753 |
|---|---|
| container_issue | 4 |
| container_start_page | 739 |
| container_title | Journal of intelligent manufacturing |
| container_volume | 25 |
| creator | Vincent, Lui Wen Han Ponnambalam, S. G. Kanagaraj, G. |
| description | Scarce resources such as material, labor, and equipment are to be optimized to improve the performance and lower production costs in flexible assembly lines. These resources are usually allocated optimally through the generation of schedules. In this paper, variants of a differential evolution-based algorithm are employed to schedule flexible assembly lines (FAL). The performance of the assembly line is optimized based on two performance criteria, namely the weighted sum of Earliness/Tardiness penalties and the balance of the assembly line. Different variants of the Bi-level differential evolution (BiDE) algorithms are developed to study the effects of three FAL problems. The parameters of BiDE algorithm for FAL problems are fine-tuned. The performance of the BiDE algorithm is evaluated using the datasets and the Bi-level Genetic Algorithm (BiGA) available in the literature. The experimental results show that the proposed differential evolution-based algorithm outperforms BiGA in terms of mean best fitness. |
| doi_str_mv | 10.1007/s10845-012-0716-8 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1671561774</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1671561774</sourcerecordid><originalsourceid>FETCH-LOGICAL-c382t-a5dbdd3f8a1e715056aad65fd21afc85d7b11951dac1e05beb9d3185da8b98933</originalsourceid><addsrcrecordid>eNp1kE1LxDAQhoMouK7-AG8FL16imbbTpkdZP2HBi55D2kw0S7Zdk3bRf2-W9SCCpxmG530ZHsbOQVyBEPV1BCFL5AJyLmqouDxgM8A65xJKPGQz0WDFEQGP2UmMKyFEIyuYscWts5YC9aPTPqPt4KfRDX221cHpfozZOGSxeyczecqsp0_XpkXHSOvWf2Xe9RRP2ZHVPtLZz5yz1_u7l8UjXz4_PC1ulrwrZD5yjaY1prBSA9WAAiutTYXW5KBtJ9HULUCDYHQHJLCltjEFpLuWbSObopizy33vJgwfE8VRrV3syHvd0zBFBVWqraCuy4Re_EFXwxT69J0CLIumFBLzRMGe6sIQYyCrNsGtdfhSINROq9prVUmr2mlVMmXyfSYmtn-j8Kv539A30RV66g</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1543940852</pqid></control><display><type>article</type><title>Differential evolution variants to schedule flexible assembly lines</title><source>SpringerLink (Online service)</source><creator>Vincent, Lui Wen Han ; Ponnambalam, S. G. ; Kanagaraj, G.</creator><creatorcontrib>Vincent, Lui Wen Han ; Ponnambalam, S. G. ; Kanagaraj, G.</creatorcontrib><description>Scarce resources such as material, labor, and equipment are to be optimized to improve the performance and lower production costs in flexible assembly lines. These resources are usually allocated optimally through the generation of schedules. In this paper, variants of a differential evolution-based algorithm are employed to schedule flexible assembly lines (FAL). The performance of the assembly line is optimized based on two performance criteria, namely the weighted sum of Earliness/Tardiness penalties and the balance of the assembly line. Different variants of the Bi-level differential evolution (BiDE) algorithms are developed to study the effects of three FAL problems. The parameters of BiDE algorithm for FAL problems are fine-tuned. The performance of the BiDE algorithm is evaluated using the datasets and the Bi-level Genetic Algorithm (BiGA) available in the literature. The experimental results show that the proposed differential evolution-based algorithm outperforms BiGA in terms of mean best fitness.</description><identifier>ISSN: 0956-5515</identifier><identifier>EISSN: 1572-8145</identifier><identifier>DOI: 10.1007/s10845-012-0716-8</identifier><language>eng</language><publisher>Boston: Springer US</publisher><subject>Algorithms ; Assembly lines ; Balancing ; Business and Management ; Control ; Equipment costs ; Evolution ; Experiments ; Fines & penalties ; Fitness ; Flexible manufacturing systems ; Genetic algorithms ; Heuristic ; Job shops ; Linear programming ; Machines ; Manufacturing ; Mathematical models ; Mechatronics ; Optimization ; Processes ; Production ; Production costs ; Production scheduling ; Robotics ; Schedules ; Scheduling ; Studies ; Work stations</subject><ispartof>Journal of intelligent manufacturing, 2014-08, Vol.25 (4), p.739-753</ispartof><rights>Springer Science+Business Media New York 2012</rights><rights>Springer Science+Business Media New York 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c382t-a5dbdd3f8a1e715056aad65fd21afc85d7b11951dac1e05beb9d3185da8b98933</citedby><cites>FETCH-LOGICAL-c382t-a5dbdd3f8a1e715056aad65fd21afc85d7b11951dac1e05beb9d3185da8b98933</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10845-012-0716-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10845-012-0716-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Vincent, Lui Wen Han</creatorcontrib><creatorcontrib>Ponnambalam, S. G.</creatorcontrib><creatorcontrib>Kanagaraj, G.</creatorcontrib><title>Differential evolution variants to schedule flexible assembly lines</title><title>Journal of intelligent manufacturing</title><addtitle>J Intell Manuf</addtitle><description>Scarce resources such as material, labor, and equipment are to be optimized to improve the performance and lower production costs in flexible assembly lines. These resources are usually allocated optimally through the generation of schedules. In this paper, variants of a differential evolution-based algorithm are employed to schedule flexible assembly lines (FAL). The performance of the assembly line is optimized based on two performance criteria, namely the weighted sum of Earliness/Tardiness penalties and the balance of the assembly line. Different variants of the Bi-level differential evolution (BiDE) algorithms are developed to study the effects of three FAL problems. The parameters of BiDE algorithm for FAL problems are fine-tuned. The performance of the BiDE algorithm is evaluated using the datasets and the Bi-level Genetic Algorithm (BiGA) available in the literature. The experimental results show that the proposed differential evolution-based algorithm outperforms BiGA in terms of mean best fitness.</description><subject>Algorithms</subject><subject>Assembly lines</subject><subject>Balancing</subject><subject>Business and Management</subject><subject>Control</subject><subject>Equipment costs</subject><subject>Evolution</subject><subject>Experiments</subject><subject>Fines & penalties</subject><subject>Fitness</subject><subject>Flexible manufacturing systems</subject><subject>Genetic algorithms</subject><subject>Heuristic</subject><subject>Job shops</subject><subject>Linear programming</subject><subject>Machines</subject><subject>Manufacturing</subject><subject>Mathematical models</subject><subject>Mechatronics</subject><subject>Optimization</subject><subject>Processes</subject><subject>Production</subject><subject>Production costs</subject><subject>Production scheduling</subject><subject>Robotics</subject><subject>Schedules</subject><subject>Scheduling</subject><subject>Studies</subject><subject>Work stations</subject><issn>0956-5515</issn><issn>1572-8145</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kE1LxDAQhoMouK7-AG8FL16imbbTpkdZP2HBi55D2kw0S7Zdk3bRf2-W9SCCpxmG530ZHsbOQVyBEPV1BCFL5AJyLmqouDxgM8A65xJKPGQz0WDFEQGP2UmMKyFEIyuYscWts5YC9aPTPqPt4KfRDX221cHpfozZOGSxeyczecqsp0_XpkXHSOvWf2Xe9RRP2ZHVPtLZz5yz1_u7l8UjXz4_PC1ulrwrZD5yjaY1prBSA9WAAiutTYXW5KBtJ9HULUCDYHQHJLCltjEFpLuWbSObopizy33vJgwfE8VRrV3syHvd0zBFBVWqraCuy4Re_EFXwxT69J0CLIumFBLzRMGe6sIQYyCrNsGtdfhSINROq9prVUmr2mlVMmXyfSYmtn-j8Kv539A30RV66g</recordid><startdate>20140801</startdate><enddate>20140801</enddate><creator>Vincent, Lui Wen Han</creator><creator>Ponnambalam, S. G.</creator><creator>Kanagaraj, G.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7TB</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>K9.</scope><scope>L.-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M0S</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20140801</creationdate><title>Differential evolution variants to schedule flexible assembly lines</title><author>Vincent, Lui Wen Han ; Ponnambalam, S. G. ; Kanagaraj, G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c382t-a5dbdd3f8a1e715056aad65fd21afc85d7b11951dac1e05beb9d3185da8b98933</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Algorithms</topic><topic>Assembly lines</topic><topic>Balancing</topic><topic>Business and Management</topic><topic>Control</topic><topic>Equipment costs</topic><topic>Evolution</topic><topic>Experiments</topic><topic>Fines & penalties</topic><topic>Fitness</topic><topic>Flexible manufacturing systems</topic><topic>Genetic algorithms</topic><topic>Heuristic</topic><topic>Job shops</topic><topic>Linear programming</topic><topic>Machines</topic><topic>Manufacturing</topic><topic>Mathematical models</topic><topic>Mechatronics</topic><topic>Optimization</topic><topic>Processes</topic><topic>Production</topic><topic>Production costs</topic><topic>Production scheduling</topic><topic>Robotics</topic><topic>Schedules</topic><topic>Scheduling</topic><topic>Studies</topic><topic>Work stations</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vincent, Lui Wen Han</creatorcontrib><creatorcontrib>Ponnambalam, S. G.</creatorcontrib><creatorcontrib>Kanagaraj, G.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Database (1962 - current)</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering 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>ABI/INFORM global</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>ProQuest Engineering Database</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>ProQuest Central Basic</collection><jtitle>Journal of intelligent manufacturing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vincent, Lui Wen Han</au><au>Ponnambalam, S. G.</au><au>Kanagaraj, G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Differential evolution variants to schedule flexible assembly lines</atitle><jtitle>Journal of intelligent manufacturing</jtitle><stitle>J Intell Manuf</stitle><date>2014-08-01</date><risdate>2014</risdate><volume>25</volume><issue>4</issue><spage>739</spage><epage>753</epage><pages>739-753</pages><issn>0956-5515</issn><eissn>1572-8145</eissn><abstract>Scarce resources such as material, labor, and equipment are to be optimized to improve the performance and lower production costs in flexible assembly lines. These resources are usually allocated optimally through the generation of schedules. In this paper, variants of a differential evolution-based algorithm are employed to schedule flexible assembly lines (FAL). The performance of the assembly line is optimized based on two performance criteria, namely the weighted sum of Earliness/Tardiness penalties and the balance of the assembly line. Different variants of the Bi-level differential evolution (BiDE) algorithms are developed to study the effects of three FAL problems. The parameters of BiDE algorithm for FAL problems are fine-tuned. The performance of the BiDE algorithm is evaluated using the datasets and the Bi-level Genetic Algorithm (BiGA) available in the literature. The experimental results show that the proposed differential evolution-based algorithm outperforms BiGA in terms of mean best fitness.</abstract><cop>Boston</cop><pub>Springer US</pub><doi>10.1007/s10845-012-0716-8</doi><tpages>15</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0956-5515 |
| ispartof | Journal of intelligent manufacturing, 2014-08, Vol.25 (4), p.739-753 |
| issn | 0956-5515 1572-8145 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1671561774 |
| source | SpringerLink (Online service) |
| subjects | Algorithms Assembly lines Balancing Business and Management Control Equipment costs Evolution Experiments Fines & penalties Fitness Flexible manufacturing systems Genetic algorithms Heuristic Job shops Linear programming Machines Manufacturing Mathematical models Mechatronics Optimization Processes Production Production costs Production scheduling Robotics Schedules Scheduling Studies Work stations |
| title | Differential evolution variants to schedule flexible assembly lines |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-29T12%3A53%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Differential%20evolution%20variants%20to%20schedule%20flexible%20assembly%20lines&rft.jtitle=Journal%20of%20intelligent%20manufacturing&rft.au=Vincent,%20Lui%20Wen%20Han&rft.date=2014-08-01&rft.volume=25&rft.issue=4&rft.spage=739&rft.epage=753&rft.pages=739-753&rft.issn=0956-5515&rft.eissn=1572-8145&rft_id=info:doi/10.1007/s10845-012-0716-8&rft_dat=%3Cproquest_cross%3E1671561774%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1543940852&rft_id=info:pmid/&rfr_iscdi=true |