Reference : Multiperiod vehicle loading with stochastic release dates
Scientific congresses and symposiums : Unpublished conference/Abstract
Business & economic sciences : Production, distribution & supply chain management
http://hdl.handle.net/2268/146900
Multiperiod vehicle loading with stochastic release dates
English
Arda, Yasemin mailto [Université de Liège - ULiège > HEC-Ecole de gestion : UER > UER Opérations : Supply Chain Management >]
Crama, Yves mailto [Université de Liège - ULiège > HEC-Ecole de gestion : UER > Recherche opérationnelle et gestion de la production >]
Kronus, David mailto [> >]
Pironet, Thierry mailto [Université de Liège - ULiège > HEC-Ecole de gestion : UER > Recherche opérationnelle et gestion de la production >]
Van Hentenryck, Pascal [Optimization Research Group NICTA, University of Melbourne > Victoria Research Laboratory > > >]
24-May-2012
27
Yes
International
Odysseus 2012
du 21 au 25 mai 2012
Christos D. Tarantilis
Mykonos
Grèce, GR
[en] Transportation ; Multiperiod ; Stochastic
[en] Production scheduling and vehicle routing problems are well-known topics in operations management. Although these tasks are consecutive in the supply chain, few optimization models tackle the associated issues. A most common situation, in practice, is actually that transportation management is disconnected from production planning: when production items or batches have been completely processed by the manufacturing plant, they become available for shipping, and they are consequently handled by the transportation managers. From a global managerial perspective, and with a view towards coordination of the product flows and customer satisfaction, this is not an ideal process. It is by far preferable, indeed, to set up an integrated production-transportation plan taking into account, among other constraints, the capacity of the plants and the customer due-dates. The present research proposes a methodology to investigate a multi-period vehicle loading problem with deterministic or stochastic information concerning items arrivals from production. Results from related optimization techniques are statistically compared and the benefits of the multi-period and stochastic modeling is demonstrated. Finally, an efficient heuristic is highlighted and is shown to be robust to the deviation from item arrival forecasts.
QuantOM
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS ; ARD-Ulg ; PRISME-HEC-Ulg ; UER OPERATIONS-HEC-Ulg ; Fonds de la Recherche Fondamentale Collective - FRFC
Researchers ; Professionals
http://hdl.handle.net/2268/146900

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