Agent-based flow-shop modelling in dynamic environment

This article studies a multiple-objective flow-shop modelling and scheduling problem by multi-agent system (MAS) in a production context characterized by diversified, high-volume production mix. The analysed flow shop is characterised by multi-machine workstations, transfer batches, sequence-depende...

Full description

Saved in:
Bibliographic Details
Published in:Production planning & control 2014-01, Vol.25 (2), p.110-122
Main Authors: Savino, Matteo M., Mazza, Antonio, Neubert, Gilles
Format: Article
Language:English
Subjects:
Agents (artificial intelligence)
Business administration
dynamic flow shop
Economics and Finance
Humanities and Social Sciences
Information systems
Job shops
multi-agent system
Production scheduling
re-entrant jobs
scheduling
Studies
Work in process
Work stations
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c402t-b65094681bc43c167ffa4307b127fabed495d44a2b67386d5c3f2c2c977658c63
cites cdi_FETCH-LOGICAL-c402t-b65094681bc43c167ffa4307b127fabed495d44a2b67386d5c3f2c2c977658c63
container_end_page 122
container_issue 2
container_start_page 110
container_title Production planning & control
container_volume 25
creator Savino, Matteo M.
Mazza, Antonio
Neubert, Gilles
description This article studies a multiple-objective flow-shop modelling and scheduling problem by multi-agent system (MAS) in a production context characterized by diversified, high-volume production mix. The analysed flow shop is characterised by multi-machine workstations, transfer batches, sequence-dependent setup times and possible re-entrant jobs. The agent-based model is structured from the current flow-shop configuration, defining the main entities and related events to realize the most possible flexible model. After a description of entities and their states, the agent framework with state diagrams is implemented through Jade platform. The system is provided with a simulation-based environment to test it in two main production scenarios including re-entrant jobs and failures. A coordination mechanism between agents and a dedicated scheduling algorithm managed by the MAS allowed to front this kind of events optimising concurrent objectives like work in process, Makespan and buffer queues.
doi_str_mv 10.1080/09537287.2013.782946
format article
fullrecord <record><control><sourceid>proquest_hal_p</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_hal_02313235v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3157718151</sourcerecordid><originalsourceid>FETCH-LOGICAL-c402t-b65094681bc43c167ffa4307b127fabed495d44a2b67386d5c3f2c2c977658c63</originalsourceid><addsrcrecordid>eNp9kEtLxDAUhYMoOI7-AxcFN7romFeTdCWDqCMMuNF1SNNkjLTJmMyD-femVF248G4uXL5zuOcAcIngDEEBb2FdEY4Fn2GIyIwLXFN2BCaIMFZWgqNjMBmQcmBOwVlKHxBCjFg9AWy-Mn5TNiqZtrBd2JfpPayLPrSm65xfFc4X7cGr3unC-J2LwfdZcA5OrOqSufjeU_D2-PB6vyiXL0_P9_NlqSnE2ZZVMP8iUKMp0YhxaxUlkDcIc6sa09K6ailVuGGcCNZWmlissa45Z5XQjEzBzej7rjq5jq5X8SCDcnIxX8rhBjFBBJNqhzJ7PbLrGD63Jm1k75LOMZQ3YZskorzKQxHN6NUf9CNso89JMsUEZXAwnQI6UjqGlKKxvx8gKIfi5U_xcihejsVn2d0oc96G2Kt9iF0rN-rQhWij8tolSf51-AISYobF</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1468460132</pqid></control><display><type>article</type><title>Agent-based flow-shop modelling in dynamic environment</title><source>Business Source Ultimate【Trial: -2024/12/31】【Remote access available】</source><source>Taylor and Francis Science and Technology Collection</source><creator>Savino, Matteo M. ; Mazza, Antonio ; Neubert, Gilles</creator><creatorcontrib>Savino, Matteo M. ; Mazza, Antonio ; Neubert, Gilles</creatorcontrib><description>This article studies a multiple-objective flow-shop modelling and scheduling problem by multi-agent system (MAS) in a production context characterized by diversified, high-volume production mix. The analysed flow shop is characterised by multi-machine workstations, transfer batches, sequence-dependent setup times and possible re-entrant jobs. The agent-based model is structured from the current flow-shop configuration, defining the main entities and related events to realize the most possible flexible model. After a description of entities and their states, the agent framework with state diagrams is implemented through Jade platform. The system is provided with a simulation-based environment to test it in two main production scenarios including re-entrant jobs and failures. A coordination mechanism between agents and a dedicated scheduling algorithm managed by the MAS allowed to front this kind of events optimising concurrent objectives like work in process, Makespan and buffer queues.</description><identifier>ISSN: 0953-7287</identifier><identifier>EISSN: 1366-5871</identifier><identifier>DOI: 10.1080/09537287.2013.782946</identifier><language>eng</language><publisher>London: Taylor &amp; Francis</publisher><subject>Agents (artificial intelligence) ; Business administration ; dynamic flow shop ; Economics and Finance ; Humanities and Social Sciences ; Information systems ; Job shops ; multi-agent system ; Production scheduling ; re-entrant jobs ; scheduling ; Studies ; Work in process ; Work stations</subject><ispartof>Production planning &amp; control, 2014-01, Vol.25 (2), p.110-122</ispartof><rights>2013 Taylor &amp; Francis 2013</rights><rights>Copyright Taylor &amp; Francis Group 2014</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c402t-b65094681bc43c167ffa4307b127fabed495d44a2b67386d5c3f2c2c977658c63</citedby><cites>FETCH-LOGICAL-c402t-b65094681bc43c167ffa4307b127fabed495d44a2b67386d5c3f2c2c977658c63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://hal.science/hal-02313235$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Savino, Matteo M.</creatorcontrib><creatorcontrib>Mazza, Antonio</creatorcontrib><creatorcontrib>Neubert, Gilles</creatorcontrib><title>Agent-based flow-shop modelling in dynamic environment</title><title>Production planning &amp; control</title><description>This article studies a multiple-objective flow-shop modelling and scheduling problem by multi-agent system (MAS) in a production context characterized by diversified, high-volume production mix. The analysed flow shop is characterised by multi-machine workstations, transfer batches, sequence-dependent setup times and possible re-entrant jobs. The agent-based model is structured from the current flow-shop configuration, defining the main entities and related events to realize the most possible flexible model. After a description of entities and their states, the agent framework with state diagrams is implemented through Jade platform. The system is provided with a simulation-based environment to test it in two main production scenarios including re-entrant jobs and failures. A coordination mechanism between agents and a dedicated scheduling algorithm managed by the MAS allowed to front this kind of events optimising concurrent objectives like work in process, Makespan and buffer queues.</description><subject>Agents (artificial intelligence)</subject><subject>Business administration</subject><subject>dynamic flow shop</subject><subject>Economics and Finance</subject><subject>Humanities and Social Sciences</subject><subject>Information systems</subject><subject>Job shops</subject><subject>multi-agent system</subject><subject>Production scheduling</subject><subject>re-entrant jobs</subject><subject>scheduling</subject><subject>Studies</subject><subject>Work in process</subject><subject>Work stations</subject><issn>0953-7287</issn><issn>1366-5871</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp9kEtLxDAUhYMoOI7-AxcFN7romFeTdCWDqCMMuNF1SNNkjLTJmMyD-femVF248G4uXL5zuOcAcIngDEEBb2FdEY4Fn2GIyIwLXFN2BCaIMFZWgqNjMBmQcmBOwVlKHxBCjFg9AWy-Mn5TNiqZtrBd2JfpPayLPrSm65xfFc4X7cGr3unC-J2LwfdZcA5OrOqSufjeU_D2-PB6vyiXL0_P9_NlqSnE2ZZVMP8iUKMp0YhxaxUlkDcIc6sa09K6ailVuGGcCNZWmlissa45Z5XQjEzBzej7rjq5jq5X8SCDcnIxX8rhBjFBBJNqhzJ7PbLrGD63Jm1k75LOMZQ3YZskorzKQxHN6NUf9CNso89JMsUEZXAwnQI6UjqGlKKxvx8gKIfi5U_xcihejsVn2d0oc96G2Kt9iF0rN-rQhWij8tolSf51-AISYobF</recordid><startdate>20140125</startdate><enddate>20140125</enddate><creator>Savino, Matteo M.</creator><creator>Mazza, Antonio</creator><creator>Neubert, Gilles</creator><general>Taylor &amp; Francis</general><general>Taylor &amp; Francis LLC</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>1XC</scope><scope>BXJBU</scope></search><sort><creationdate>20140125</creationdate><title>Agent-based flow-shop modelling in dynamic environment</title><author>Savino, Matteo M. ; Mazza, Antonio ; Neubert, Gilles</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c402t-b65094681bc43c167ffa4307b127fabed495d44a2b67386d5c3f2c2c977658c63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Agents (artificial intelligence)</topic><topic>Business administration</topic><topic>dynamic flow shop</topic><topic>Economics and Finance</topic><topic>Humanities and Social Sciences</topic><topic>Information systems</topic><topic>Job shops</topic><topic>multi-agent system</topic><topic>Production scheduling</topic><topic>re-entrant jobs</topic><topic>scheduling</topic><topic>Studies</topic><topic>Work in process</topic><topic>Work stations</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Savino, Matteo M.</creatorcontrib><creatorcontrib>Mazza, Antonio</creatorcontrib><creatorcontrib>Neubert, Gilles</creatorcontrib><collection>CrossRef</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>HAL-SHS: Archive ouverte en Sciences de l'Homme et de la Société</collection><jtitle>Production planning &amp; control</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Savino, Matteo M.</au><au>Mazza, Antonio</au><au>Neubert, Gilles</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Agent-based flow-shop modelling in dynamic environment</atitle><jtitle>Production planning &amp; control</jtitle><date>2014-01-25</date><risdate>2014</risdate><volume>25</volume><issue>2</issue><spage>110</spage><epage>122</epage><pages>110-122</pages><issn>0953-7287</issn><eissn>1366-5871</eissn><abstract>This article studies a multiple-objective flow-shop modelling and scheduling problem by multi-agent system (MAS) in a production context characterized by diversified, high-volume production mix. The analysed flow shop is characterised by multi-machine workstations, transfer batches, sequence-dependent setup times and possible re-entrant jobs. The agent-based model is structured from the current flow-shop configuration, defining the main entities and related events to realize the most possible flexible model. After a description of entities and their states, the agent framework with state diagrams is implemented through Jade platform. The system is provided with a simulation-based environment to test it in two main production scenarios including re-entrant jobs and failures. A coordination mechanism between agents and a dedicated scheduling algorithm managed by the MAS allowed to front this kind of events optimising concurrent objectives like work in process, Makespan and buffer queues.</abstract><cop>London</cop><pub>Taylor &amp; Francis</pub><doi>10.1080/09537287.2013.782946</doi><tpages>13</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0953-7287
ispartof Production planning & control, 2014-01, Vol.25 (2), p.110-122
issn 0953-7287
1366-5871
language eng
recordid cdi_hal_primary_oai_HAL_hal_02313235v1
source Business Source Ultimate【Trial: -2024/12/31】【Remote access available】; Taylor and Francis Science and Technology Collection
subjects Agents (artificial intelligence)
Business administration
dynamic flow shop
Economics and Finance
Humanities and Social Sciences
Information systems
Job shops
multi-agent system
Production scheduling
re-entrant jobs
scheduling
Studies
Work in process
Work stations
title Agent-based flow-shop modelling in dynamic environment
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T21%3A03%3A04IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_hal_p&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Agent-based%20flow-shop%20modelling%20in%20dynamic%20environment&rft.jtitle=Production%20planning%20&%20control&rft.au=Savino,%20Matteo%20M.&rft.date=2014-01-25&rft.volume=25&rft.issue=2&rft.spage=110&rft.epage=122&rft.pages=110-122&rft.issn=0953-7287&rft.eissn=1366-5871&rft_id=info:doi/10.1080/09537287.2013.782946&rft_dat=%3Cproquest_hal_p%3E3157718151%3C/proquest_hal_p%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c402t-b65094681bc43c167ffa4307b127fabed495d44a2b67386d5c3f2c2c977658c63%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1468460132&rft_id=info:pmid/&rfr_iscdi=true