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dc.contributor.author
Juan, A.
dc.contributor.author
Faulin, J.
dc.contributor.author
Grasman, S.
dc.contributor.author
Riera, D.
dc.contributor.author
Marull, J.
dc.contributor.author
Mendez, Carlos Alberto
dc.date.available
2017-02-14T20:28:16Z
dc.date.issued
2011-12
dc.identifier.citation
Juan, A.; Faulin, J.; Grasman, S.; Riera, D.; Marull, J.; et al.; Using safety stocks and simulation to solve the vehicle routing problem with stochastic demands; Elsevier; Transportation Research. Part C, Emerging Technologies; 19; 5; 12-2011; 751-765
dc.identifier.issn
0968-090X
dc.identifier.uri
http://hdl.handle.net/11336/13006
dc.description.abstract
After introducing the Vehicle Routing Problem with Stochastic Demands (VRPSD) and some related work, this paper proposes a flexible solution methodology. The logic behind this methodology is to transform the issue of solving a given VRPSD instance into an issue of solving a small set of Capacitated Vehicle Routing Problem (CVRP) instances. Thus, our approach takes advantage of the fact that extremely efficient metaheuristics for the CVRP already exists. The CVRP instances are obtained from the original VRPSD instance by assigning different values to the level of safety stocks that routed vehicles must employ to deal with unexpected demands. The methodology also makes use of Monte Carlo simulation (MCS) to obtain estimates of the reliability of each aprioristic solution – that is, the probability that no vehicle runs out of load before completing its delivering route – as well as for the expected costs associated with corrective routing actions (recourse actions) after a vehicle runs out of load before completing its route. This way, estimates for expected total costs of different routing alternatives are obtained. Finally, an extensive numerical experiment is included in the paper with the purpose of analyzing the efficiency of the described methodology under different uncertainty scenarios
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
Vehicle Routing Problem with Stochastic Demands
dc.subject
Monte Carlo Simulation
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Reliability Indices
dc.subject
Metaheuristics
dc.subject.classification
Otras Ingenierías y Tecnologías
dc.subject.classification
Otras Ingenierías y Tecnologías
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Using safety stocks and simulation to solve the vehicle routing problem with stochastic demands
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-02-09T13:51:35Z
dc.journal.volume
19
dc.journal.number
5
dc.journal.pagination
751-765
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Juan, A.. Open University of Catalonia; España
dc.description.fil
Fil: Faulin, J.. Public University of Navarre; España
dc.description.fil
Fil: Grasman, S.. Missouri University of Science & Technology; Estados Unidos
dc.description.fil
Fil: Riera, D.. Open University of Catalonia; España
dc.description.fil
Fil: Marull, J.. Open University of Catalonia; España
dc.description.fil
Fil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); Argentina
dc.journal.title
Transportation Research. Part C, Emerging Technologies
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.trc.2010.09.007
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0968090X10001439
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