Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part I: MINLP and MILP models
Identifieur interne : 005151 ( Main/Exploration ); précédent : 005150; suivant : 005152Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part I: MINLP and MILP models
Auteurs : Maria Analia Rodriguez [Argentine] ; Aldo R. Vecchietti [Argentine] ; Iiro Harjunkoski [Allemagne] ; Ignacio E. Grossmann [États-Unis]Source :
- Computers & chemical engineering [ 0098-1354 ] ; 2014.
Descripteurs français
- Pascal (Inist)
- Conception optimale, Plan expérience, Logistique, Périodique, Offre et demande, Gestion production, Programmation partiellement en nombres entiers, Programmation non linéaire, Conception ingénierie, Pièce rechange, Gestion risque, Planification, Entrepôt, Entreposage, Production multiple, Gestion stock, Stock sécurité, Maillon chaîne, Contrainte capacité, Système incertain, Service après vente, Optimisation, Modélisation, Long terme, Qualité service, Service utilisateur, Mutualisme, Solution bornée, Usine fabrication, ..
- Wicri :
- topic : Offre et demande.
English descriptors
- KwdEn :
- After sale service, Bounded solution, Capacity constraint, Chain link, Engineering design, Experimental design, Factory, Inventory control, Logistics, Long term, Mixed integer programming, Modeling, Multiple production, Mutualism, Non linear programming, Optimal design, Optimization, Periodical, Planning, Production management, Risk management, Safety stock, Service quality, Spare part, Supply demand balance, Uncertain system, User service, Warehouse, Warehousing.
Abstract
An optimization model is proposed to redesign the supply chain of spare part delivery under demand uncertainty from strategic and tactical perspectives in a planning horizon consisting of multiple periods. Long term decisions involve new installations, expansions and elimination of warehouses and factories handling multiple products. It is also considered which warehouses should be used as repair work-shops in order to store, repair and deliver used units to customers. Tactical planning includes deciding inventory levels (safety stock and expected inventory) for each type of spare part in distribution centers and customer plants, as well as the connection links between the supply chain nodes. Capacity constraints are also taken into account when planning inventory levels. At the tactical level it is determined how demand of failing units is satisfied, and whether to use new or used parts. The uncertain demand is addressed by defining the optimal amount of safety stock that guarantees certain service level at a customer plant. In addition, the risk-pooling effect is taken into account when defining inventory levels in distribution centers and customer zones. Due to the nonlinear nature of the original formulation, a piece-wise linearization approach is applied to obtain a tight lower bound of the optimal solution. The formulation can be adapted to several industry-critical units and the supply chain of electric motors is provided here as an example.
Affiliations:
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Part I: MINLP and MILP models</title><author><name sortKey="Rodriguez, Maria Analia" sort="Rodriguez, Maria Analia" uniqKey="Rodriguez M" first="Maria Analia" last="Rodriguez">Maria Analia Rodriguez</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>INGAR</s1><s2>Avellaneda 3657, S3002GJC Santa Fe</s2><s3>ARG</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>Argentine</country><wicri:noRegion>INGAR</wicri:noRegion></affiliation></author><author><name sortKey="Vecchietti, Aldo R" sort="Vecchietti, Aldo R" uniqKey="Vecchietti A" first="Aldo R." last="Vecchietti">Aldo R. Vecchietti</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>INGAR</s1><s2>Avellaneda 3657, S3002GJC Santa Fe</s2><s3>ARG</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>Argentine</country><wicri:noRegion>INGAR</wicri:noRegion></affiliation></author><author><name sortKey="Harjunkoski, Iiro" sort="Harjunkoski, Iiro" uniqKey="Harjunkoski I" first="Iiro" last="Harjunkoski">Iiro Harjunkoski</name><affiliation wicri:level="1"><inist:fA14 i1="02"><s1>ABB Corporate Research, Wallstadter Str. 59</s1><s2>68526 Ladenburg</s2><s3>DEU</s3><sZ>3 aut.</sZ></inist:fA14><country>Allemagne</country><wicri:noRegion>68526 Ladenburg</wicri:noRegion><wicri:noRegion>Wallstadter Str. 59</wicri:noRegion><wicri:noRegion>68526 Ladenburg</wicri:noRegion></affiliation></author><author><name sortKey="Grossmann, Ignacio E" sort="Grossmann, Ignacio E" uniqKey="Grossmann I" first="Ignacio E." last="Grossmann">Ignacio E. Grossmann</name><affiliation wicri:level="4"><inist:fA14 i1="03"><s1>Department of Chemical Engineering, Carnegie Mellon University</s1><s2>Pittsburgh, PA 15213</s2><s3>USA</s3><sZ>4 aut.</sZ></inist:fA14><country>États-Unis</country><placeName><settlement type="city">Pittsburgh</settlement><region type="state">Pennsylvanie</region></placeName><orgName type="university">Université Carnegie-Mellon</orgName></affiliation></author></analytic><series><title level="j" type="main">Computers & chemical engineering</title><title level="j" type="abbreviated">Comput. chem. eng.</title><idno type="ISSN">0098-1354</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Computers & chemical engineering</title><title level="j" type="abbreviated">Comput. chem. eng.</title><idno type="ISSN">0098-1354</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>After sale service</term><term>Bounded solution</term><term>Capacity constraint</term><term>Chain link</term><term>Engineering design</term><term>Experimental design</term><term>Factory</term><term>Inventory control</term><term>Logistics</term><term>Long term</term><term>Mixed integer programming</term><term>Modeling</term><term>Multiple production</term><term>Mutualism</term><term>Non linear programming</term><term>Optimal design</term><term>Optimization</term><term>Periodical</term><term>Planning</term><term>Production management</term><term>Risk management</term><term>Safety stock</term><term>Service quality</term><term>Spare part</term><term>Supply demand balance</term><term>Uncertain system</term><term>User service</term><term>Warehouse</term><term>Warehousing</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Conception optimale</term><term>Plan expérience</term><term>Logistique</term><term>Périodique</term><term>Offre et demande</term><term>Gestion production</term><term>Programmation partiellement en nombres entiers</term><term>Programmation non linéaire</term><term>Conception ingénierie</term><term>Pièce rechange</term><term>Gestion risque</term><term>Planification</term><term>Entrepôt</term><term>Entreposage</term><term>Production multiple</term><term>Gestion stock</term><term>Stock sécurité</term><term>Maillon chaîne</term><term>Contrainte capacité</term><term>Système incertain</term><term>Service après vente</term><term>Optimisation</term><term>Modélisation</term><term>Long terme</term><term>Qualité service</term><term>Service utilisateur</term><term>Mutualisme</term><term>Solution bornée</term><term>Usine fabrication</term><term>.</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Offre et demande</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">An optimization model is proposed to redesign the supply chain of spare part delivery under demand uncertainty from strategic and tactical perspectives in a planning horizon consisting of multiple periods. Long term decisions involve new installations, expansions and elimination of warehouses and factories handling multiple products. It is also considered which warehouses should be used as repair work-shops in order to store, repair and deliver used units to customers. Tactical planning includes deciding inventory levels (safety stock and expected inventory) for each type of spare part in distribution centers and customer plants, as well as the connection links between the supply chain nodes. Capacity constraints are also taken into account when planning inventory levels. At the tactical level it is determined how demand of failing units is satisfied, and whether to use new or used parts. The uncertain demand is addressed by defining the optimal amount of safety stock that guarantees certain service level at a customer plant. In addition, the risk-pooling effect is taken into account when defining inventory levels in distribution centers and customer zones. Due to the nonlinear nature of the original formulation, a piece-wise linearization approach is applied to obtain a tight lower bound of the optimal solution. The formulation can be adapted to several industry-critical units and the supply chain of electric motors is provided here as an example.</div></front></TEI><affiliations><list><country><li>Allemagne</li><li>Argentine</li><li>États-Unis</li></country><region><li>Pennsylvanie</li></region><settlement><li>Pittsburgh</li></settlement><orgName><li>Université Carnegie-Mellon</li></orgName></list><tree><country name="Argentine"><noRegion><name sortKey="Rodriguez, Maria Analia" sort="Rodriguez, Maria Analia" uniqKey="Rodriguez M" first="Maria Analia" last="Rodriguez">Maria Analia Rodriguez</name></noRegion><name sortKey="Vecchietti, Aldo R" sort="Vecchietti, Aldo R" uniqKey="Vecchietti A" first="Aldo R." last="Vecchietti">Aldo R. Vecchietti</name></country><country name="Allemagne"><noRegion><name sortKey="Harjunkoski, Iiro" sort="Harjunkoski, Iiro" uniqKey="Harjunkoski I" first="Iiro" last="Harjunkoski">Iiro Harjunkoski</name></noRegion></country><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Grossmann, Ignacio E" sort="Grossmann, Ignacio E" uniqKey="Grossmann I" first="Ignacio E." last="Grossmann">Ignacio E. Grossmann</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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