Min-max and predictive control for the management of distribution in supply chains (Articolo in rivista)

Type
Label
  • Min-max and predictive control for the management of distribution in supply chains (Articolo in rivista) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/TCST.2010.2076283 (literal)
Alternative label
  • A. Alessandri; M. Gaggero; F. Tonelli (2011)
    Min-max and predictive control for the management of distribution in supply chains
    in IEEE transactions on control systems technology (Print); IEEE-Institute Of Electrical And Electronics Engineers Inc., Piscataway (Stati Uniti d'America)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • A. Alessandri; M. Gaggero; F. Tonelli (literal)
Pagina inizio
  • 1075 (literal)
Pagina fine
  • 1089 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Journal Q1 in Control and Systems Engineering (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 19 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 15 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 5 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1. Department of Production Engineering, Thermoenergetics, and Mathematical Models (DIPTEM), University of Genoa, I-16129 Genoa, Italy 2. Department of Production Engineering, Thermoenergetics, and Mathematical Models (DIPTEM), University of Genoa, I-16129 Genoa, Italy 3. Department of Production Engineering, Thermoenergetics, and Mathematical Models (DIPTEM), University of Genoa, I-16129 Genoa, Italy (literal)
Titolo
  • Min-max and predictive control for the management of distribution in supply chains (literal)
Abstract
  • Inventory control for the management of multi-item multi-echelon distribution chains is addressed in a two-level hierarchical framework motivated by strategic and tactical points of view. Toward this end, a discrete-time dynamic model is presented together with various types of constraints to describe a generic distribution chain in detail. As to the strategic level, a worst-case approach is proposed to set up a stock replenishment policy by using the uncertain information available on long-term predictions of customers' demands. The solution of the resulting min-max problem is obtained by using a branch-and-bound algorithm to select policy parameters such as safety stocks and delivery cycle times of goods. The online decisions on the transportation of goods are made at the tactical level instead. In order to accomplish such a task, an approach based on model predictive control is proposed to exploit recent, more reliable, short-term predictions of the de- mands. Simulation results are presented to show the effectiveness and potential of the proposed methodology. (literal)
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