A simulation optimization methodology for supplier selection problem
Identifieur interne : 000965 ( Hal/Corpus ); précédent : 000964; suivant : 000966A simulation optimization methodology for supplier selection problem
Auteurs : Hongwei Ding ; Lyès Benyoucef ; Xiaolan XieSource :
- International Journal of Computer Integrated Manufacturing [ 0951-192X ] ; 2005.
English descriptors
- mix :
Abstract
Strategic sourcing plays a critical role in supply chain planning. Supplier selection is one of the decisions that determine the long-term viability of a company. In this paper, a new simulation optimization methodology is presented to make decisions on supplier selection. The methodology is composed of three basic modules: a genetic algorithm (GA) optimizer, a discrete-event simulator and a supply chain modelling framework. The GA optimizer continuously search different supplier portfolio and related operation parameters. Corresponding simulation models are automatically created through an object-oriented process. After simulation runs, the fitness value of candidate supplier portfolio is derived from the estimations of key performance indicators (KPI). The fitness is returned to the GA to be utilized in searching the next prominent direction. By using the proposed methodology, the supply chain planner is able to optimize the supplier portfolio with taking uncertainties into consideration. Finally, a real-life case study is presented to illustrate the applicability of the proposed methodology. Experimental results are presented and analysed.
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DOI: 10.1080/0951192052000288161
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Hal:emse-00449389Le document en format XML
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Supplier selection is one of the decisions that determine the long-term viability of a company. In this paper, a new simulation optimization methodology is presented to make decisions on supplier selection. The methodology is composed of three basic modules: a genetic algorithm (GA) optimizer, a discrete-event simulator and a supply chain modelling framework. The GA optimizer continuously search different supplier portfolio and related operation parameters. Corresponding simulation models are automatically created through an object-oriented process. After simulation runs, the fitness value of candidate supplier portfolio is derived from the estimations of key performance indicators (KPI). The fitness is returned to the GA to be utilized in searching the next prominent direction. By using the proposed methodology, the supply chain planner is able to optimize the supplier portfolio with taking uncertainties into consideration. Finally, a real-life case study is presented to illustrate the applicability of the proposed methodology. Experimental results are presented and analysed.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">A simulation optimization methodology for supplier selection problem</title><author role="aut"><persName><forename type="first">Hongwei</forename><surname>Ding</surname></persName><email></email><idno type="halauthor">99703</idno><affiliation ref="#struct-2353"></affiliation></author><author role="aut"><persName><forename type="first">Lyès</forename><surname>Benyoucef</surname></persName><email></email><idno type="halauthor">99702</idno><affiliation ref="#struct-2353"></affiliation></author><author role="aut"><persName><forename type="first">Xiaolan</forename><surname>Xie</surname></persName><email>xie@emse.fr</email><idno type="halauthor">459490</idno><affiliation ref="#struct-22135"></affiliation><affiliation ref="#struct-56975"></affiliation><affiliation ref="#struct-69111"></affiliation></author><editor role="depositor"><persName><forename>Andrée-Aimée</forename><surname>Toucas</surname></persName><email>toucas@emse.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2010-01-21 14:46:17</date><date type="whenModified">2016-05-19 01:09:44</date><date type="whenReleased">2010-01-21 14:46:17</date><date type="whenProduced">2005</date></edition><respStmt><resp>contributor</resp><name key="136360"><persName><forename>Andrée-Aimée</forename><surname>Toucas</surname></persName><email>toucas@emse.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">emse-00449389</idno><idno type="halUri">http://hal-emse.ccsd.cnrs.fr/emse-00449389</idno><idno type="halBibtex">ding:emse-00449389</idno><idno type="halRefHtml">International Journal of Computer Integrated Manufacturing, Taylor & Francis, 2005, 18 (2-3), pp.210-224. <10.1080/0951192052000288161></idno><idno type="halRef">International Journal of Computer Integrated Manufacturing, Taylor & Francis, 2005, 18 (2-3), pp.210-224. <10.1080/0951192052000288161></idno></publicationStmt><seriesStmt><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="CIS-ENSMSE" p="EMSE">CIS - Centre Ingénierie et Santé</idno><idno type="stamp" n="GIH-ENSMSE" p="CIS-ENSMSE">CIS / GIH : Département Génie Industriel Hospitalier</idno><idno type="stamp" n="EMSE">Ecole Nationale Supérieure des Mines de Saint-Etienne</idno><idno type="stamp" n="IFRESIS-ENSMSE" p="EMSE">IFR 143 - Institut Fédératif de Recherche en Sciences et Ingénierie de la Santé</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="INSTITUT-TELECOM">Institut Télécom</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A simulation optimization methodology for supplier selection problem</title><author role="aut"><persName><forename type="first">Hongwei</forename><surname>Ding</surname></persName><idno type="halAuthorId">99703</idno><affiliation ref="#struct-2353"></affiliation></author><author role="aut"><persName><forename type="first">Lyès</forename><surname>Benyoucef</surname></persName><idno type="halAuthorId">99702</idno><affiliation ref="#struct-2353"></affiliation></author><author role="aut"><persName><forename type="first">Xiaolan</forename><surname>Xie</surname></persName><email>xie@emse.fr</email><idno type="halAuthorId">459490</idno><affiliation ref="#struct-22135"></affiliation><affiliation ref="#struct-56975"></affiliation><affiliation ref="#struct-69111"></affiliation></author></analytic><monogr><idno type="halJournalId" status="VALID">12390</idno><idno type="issn">0951-192X</idno><idno type="eissn">1029-0257</idno><title level="j">International Journal of Computer Integrated Manufacturing</title><imprint><publisher>Taylor & Francis</publisher><biblScope unit="volume">18</biblScope><biblScope unit="issue">2-3</biblScope><biblScope unit="pp">210-224</biblScope><date type="datePub">2005</date></imprint></monogr><idno type="doi">10.1080/0951192052000288161</idno></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Genetic algorithm approach</term><term xml:lang="en">analytic hierarchy process</term><term xml:lang="en">multiobjective approach</term><term xml:lang="en">choice strategies</term><term xml:lang="en">decision-support</term><term xml:lang="en">vendor selection</term><term xml:lang="en">management</term><term xml:lang="en">chains</term></keywords><classCode scheme="halDomain" n="spi.gproc">Engineering Sciences [physics]/Chemical and Process Engineering</classCode><classCode scheme="halDomain" n="sdv.ib">Life Sciences [q-bio]/Bioengineering</classCode><classCode scheme="halTypology" n="ART">Journal articles</classCode></textClass><abstract xml:lang="en">Strategic sourcing plays a critical role in supply chain planning. Supplier selection is one of the decisions that determine the long-term viability of a company. In this paper, a new simulation optimization methodology is presented to make decisions on supplier selection. The methodology is composed of three basic modules: a genetic algorithm (GA) optimizer, a discrete-event simulator and a supply chain modelling framework. The GA optimizer continuously search different supplier portfolio and related operation parameters. Corresponding simulation models are automatically created through an object-oriented process. After simulation runs, the fitness value of candidate supplier portfolio is derived from the estimations of key performance indicators (KPI). The fitness is returned to the GA to be utilized in searching the next prominent direction. By using the proposed methodology, the supply chain planner is able to optimize the supplier portfolio with taking uncertainties into consideration. Finally, a real-life case study is presented to illustrate the applicability of the proposed methodology. Experimental results are presented and analysed.</abstract><particDesc><org type="consortium">INRIA-LORRAINE Metz</org></particDesc></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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