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Using parallel & distributed computing for solving real-time vehicle routing problems with stochastic demands
dc.contributor.author | Juan Pérez, Angel Alejandro |
dc.contributor.author | Faulín, Javier |
dc.contributor.author | Jorba, Josep |
dc.contributor.author | Caceres, J |
dc.contributor.author | Marquès Puig, Joan Manel |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Matemàtica Aplicada I |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.date.accessioned | 2013-01-14T16:39:55Z |
dc.date.created | 2011 |
dc.date.issued | 2011 |
dc.identifier.citation | Juan, A. [et al.]. Using parallel & distributed computing for solving real-time vehicle routing problems with stochastic demands. "Annals of operations research", 2011, núm. June, p. 1-23. |
dc.identifier.issn | 0254-5330 |
dc.identifier.uri | http://hdl.handle.net/2117/17349 |
dc.description.abstract | This paper focuses on the Vehicle Routing Problem with Stochastic Demands (VRPSD) and discusses how Parallel and Distributed Computing Systems can be employed to efficiently solve the VRPSD. Our approach deals with uncertainty in the customer demands by considering safety stocks, i.e. when designing the routes, part of the vehicle capacity is reserved to deal with potential emergency situations caused by unexpected demands. Thus, for a given VRPSD instance, our algorithm considers different levels of safety stocks. For each of these levels, a different scenario is defined. Then, the algorithm solves each scenario by integrating Monte Carlo simulation inside a heuristic-randomization process. This way, expected variable costs due to route failures can be naturally estimated even when customers’ demands follow a non-normal probability distribution. Use of parallelization strategies is then considered to run multiple instances of the algorithm in a concurrent way. The resulting concurrent solutions are then compared and the one with the minimum total costs is selected. Two numerical experiments allow analyzing the algorithm’s performance under different parallelization schemas. |
dc.format.extent | 23 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa |
dc.subject.lcsh | Operations research |
dc.title | Using parallel & distributed computing for solving real-time vehicle routing problems with stochastic demands |
dc.type | Article |
dc.subject.lemac | Investigació operativa |
dc.identifier.doi | 10.1007/s10479-011-0918-z |
dc.description.peerreviewed | Peer Reviewed |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 11158086 |
dc.description.version | Postprint (published version) |
dc.date.lift | 10000-01-01 |
local.citation.author | Juan, A.; Faulín, J.; Jorba, J.; Caceres, J.; Marques, J. |
local.citation.publicationName | Annals of operations research |
local.citation.number | June |
local.citation.startingPage | 1 |
local.citation.endingPage | 23 |
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