Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence
In many manufacturing environments, automated guided vehicles are used to move the processed materials between various pickup and delivery points. The assignment of vehicles to unit loads is a complex problem that is often solved in real-time with simple dispatching rules. This paper proposes an aut...
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| Veröffentlicht in: | IEEE transactions on cybernetics 2005-04, Vol.35 (2), p.208-226 |
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| creator | Naso, D. Turchiano, B. |
| description | In many manufacturing environments, automated guided vehicles are used to move the processed materials between various pickup and delivery points. The assignment of vehicles to unit loads is a complex problem that is often solved in real-time with simple dispatching rules. This paper proposes an automated guided vehicles dispatching approach based on computational intelligence. We adopt a fuzzy multicriteria decision strategy to simultaneously take into account multiple aspects in every dispatching decision. Since the typical short-term view of dispatching rules is one of the main limitations of such real-time assignment heuristics, we also incorporate in the multicriteria algorithm a specific heuristic rule that takes into account the empty-vehicle travel on a longer time-horizon. Moreover, we also adopt a genetic algorithm to tune the weights associated to each decision criteria in the global decision algorithm. The proposed approach is validated by means of a comparison with other dispatching rules, and with other recently proposed multicriteria dispatching strategies also based on computational Intelligence. The analysis of the results obtained by the proposed dispatching approach in both nominal and perturbed operating conditions (congestions, faults) confirms its effectiveness. |
| doi_str_mv | 10.1109/TSMCB.2004.842249 |
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The assignment of vehicles to unit loads is a complex problem that is often solved in real-time with simple dispatching rules. This paper proposes an automated guided vehicles dispatching approach based on computational intelligence. We adopt a fuzzy multicriteria decision strategy to simultaneously take into account multiple aspects in every dispatching decision. Since the typical short-term view of dispatching rules is one of the main limitations of such real-time assignment heuristics, we also incorporate in the multicriteria algorithm a specific heuristic rule that takes into account the empty-vehicle travel on a longer time-horizon. Moreover, we also adopt a genetic algorithm to tune the weights associated to each decision criteria in the global decision algorithm. The proposed approach is validated by means of a comparison with other dispatching rules, and with other recently proposed multicriteria dispatching strategies also based on computational Intelligence. 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(IEEE) 2005</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c507t-6f582c1e5853deab39ff7b75fecf2fe6629e07369b7dae7bdbe77a6553660cdf3</citedby><cites>FETCH-LOGICAL-c507t-6f582c1e5853deab39ff7b75fecf2fe6629e07369b7dae7bdbe77a6553660cdf3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1408052$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1408052$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/15828651$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Naso, D.</creatorcontrib><creatorcontrib>Turchiano, B.</creatorcontrib><title>Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence</title><title>IEEE transactions on cybernetics</title><addtitle>TSMCB</addtitle><addtitle>IEEE Trans Syst Man Cybern B Cybern</addtitle><description>In many manufacturing environments, automated guided vehicles are used to move the processed materials between various pickup and delivery points. The assignment of vehicles to unit loads is a complex problem that is often solved in real-time with simple dispatching rules. This paper proposes an automated guided vehicles dispatching approach based on computational intelligence. We adopt a fuzzy multicriteria decision strategy to simultaneously take into account multiple aspects in every dispatching decision. Since the typical short-term view of dispatching rules is one of the main limitations of such real-time assignment heuristics, we also incorporate in the multicriteria algorithm a specific heuristic rule that takes into account the empty-vehicle travel on a longer time-horizon. Moreover, we also adopt a genetic algorithm to tune the weights associated to each decision criteria in the global decision algorithm. The proposed approach is validated by means of a comparison with other dispatching rules, and with other recently proposed multicriteria dispatching strategies also based on computational Intelligence. The analysis of the results obtained by the proposed dispatching approach in both nominal and perturbed operating conditions (congestions, faults) confirms its effectiveness.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Automated guided vehicles</subject><subject>Automated guided vehicles (AGVs)</subject><subject>Automatic control</subject><subject>Biomimetics - methods</subject><subject>Computation</subject><subject>Computational intelligence</subject><subject>Computer Simulation</subject><subject>Control systems</subject><subject>Decision Support Techniques</subject><subject>Dispatching</subject><subject>Dispatching rules</subject><subject>Electrical equipment industry</subject><subject>Feedback</subject><subject>fuzzy control</subject><subject>Genetic algorithms</subject><subject>Heuristic</subject><subject>Information Storage and Retrieval - methods</subject><subject>Intelligence</subject><subject>Intelligent vehicles</subject><subject>Manufacturing automation</subject><subject>Manufacturing systems</subject><subject>Models, Theoretical</subject><subject>Movement</subject><subject>Optimal control</subject><subject>Real time</subject><subject>Robotics - methods</subject><subject>Strategy</subject><subject>Studies</subject><subject>Vehicles</subject><issn>1083-4419</issn><issn>2168-2267</issn><issn>1941-0492</issn><issn>2168-2275</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqNkc9rFTEQxxdRbK3-ASLI4qF42eckm5_H-qhVaPFg9SYhm520KbubZ5I9-N-b-h4UPKinGSafGb7k0zQvCWwIAf3u-svV9v2GArCNYpQy_ag5JpqRDpimj2sPqu8YI_qoeZbzHQBo0PJpc0S4okpwctx8v1qnElwKBVOw7YzFdre4ppDrNLc-pvbs4ls7hryzxd2G5aZ1cSkpTu1gM45tXOpg3q3FlhAXO7VhKThN4QYXh8-bJ95OGV8c6knz9cP59fZjd_n54tP27LJzHGTphK95HEGueD-iHXrtvRwk9-g89SgE1QiyF3qQo0U5jANKaQXnvRDgRt-fNKf7u7sUf6yYi5lDdjWGXTCu2QgpGQfK_wlSpQUoJv4DBEGAywq-_StIhCRM9ULSir75A72La6p_lo0SUtSIhFSI7CGXYs4JvdmlMNv00xAw99bNb-vm3rrZW687rw-H12HG8WHjoLkCr_ZAQMSHZwYKOO1_AWNnsX8</recordid><startdate>20050401</startdate><enddate>20050401</enddate><creator>Naso, D.</creator><creator>Turchiano, B.</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>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</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></search><sort><creationdate>20050401</creationdate><title>Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence</title><author>Naso, D. ; Turchiano, B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c507t-6f582c1e5853deab39ff7b75fecf2fe6629e07369b7dae7bdbe77a6553660cdf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Automated guided vehicles</topic><topic>Automated guided vehicles (AGVs)</topic><topic>Automatic control</topic><topic>Biomimetics - methods</topic><topic>Computation</topic><topic>Computational intelligence</topic><topic>Computer Simulation</topic><topic>Control systems</topic><topic>Decision Support Techniques</topic><topic>Dispatching</topic><topic>Dispatching rules</topic><topic>Electrical equipment industry</topic><topic>Feedback</topic><topic>fuzzy control</topic><topic>Genetic algorithms</topic><topic>Heuristic</topic><topic>Information Storage and Retrieval - methods</topic><topic>Intelligence</topic><topic>Intelligent vehicles</topic><topic>Manufacturing automation</topic><topic>Manufacturing systems</topic><topic>Models, Theoretical</topic><topic>Movement</topic><topic>Optimal control</topic><topic>Real time</topic><topic>Robotics - methods</topic><topic>Strategy</topic><topic>Studies</topic><topic>Vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Naso, D.</creatorcontrib><creatorcontrib>Turchiano, B.</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>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</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>Naso, D.</au><au>Turchiano, B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence</atitle><jtitle>IEEE transactions on cybernetics</jtitle><stitle>TSMCB</stitle><addtitle>IEEE Trans Syst Man Cybern B Cybern</addtitle><date>2005-04-01</date><risdate>2005</risdate><volume>35</volume><issue>2</issue><spage>208</spage><epage>226</epage><pages>208-226</pages><issn>1083-4419</issn><issn>2168-2267</issn><eissn>1941-0492</eissn><eissn>2168-2275</eissn><coden>ITSCFI</coden><abstract>In many manufacturing environments, automated guided vehicles are used to move the processed materials between various pickup and delivery points. The assignment of vehicles to unit loads is a complex problem that is often solved in real-time with simple dispatching rules. This paper proposes an automated guided vehicles dispatching approach based on computational intelligence. We adopt a fuzzy multicriteria decision strategy to simultaneously take into account multiple aspects in every dispatching decision. Since the typical short-term view of dispatching rules is one of the main limitations of such real-time assignment heuristics, we also incorporate in the multicriteria algorithm a specific heuristic rule that takes into account the empty-vehicle travel on a longer time-horizon. Moreover, we also adopt a genetic algorithm to tune the weights associated to each decision criteria in the global decision algorithm. The proposed approach is validated by means of a comparison with other dispatching rules, and with other recently proposed multicriteria dispatching strategies also based on computational Intelligence. The analysis of the results obtained by the proposed dispatching approach in both nominal and perturbed operating conditions (congestions, faults) confirms its effectiveness.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>15828651</pmid><doi>10.1109/TSMCB.2004.842249</doi><tpages>19</tpages></addata></record> |
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| subjects | Algorithms Artificial Intelligence Automated guided vehicles Automated guided vehicles (AGVs) Automatic control Biomimetics - methods Computation Computational intelligence Computer Simulation Control systems Decision Support Techniques Dispatching Dispatching rules Electrical equipment industry Feedback fuzzy control Genetic algorithms Heuristic Information Storage and Retrieval - methods Intelligence Intelligent vehicles Manufacturing automation Manufacturing systems Models, Theoretical Movement Optimal control Real time Robotics - methods Strategy Studies Vehicles |
| title | Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence |
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