SIM-Randsharp: A hybrid algorithm for solving the ARC routing problem with stochastic demands

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dc.contributor	Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.contributor	Universitat Politècnica de Catalunya. LIAM - Laboratori de Modelització i Anàlisi de la Informació
dc.contributor.author	González, Sergio
dc.contributor.author	Riera, Daniel
dc.contributor.author	Juan, Angel A.
dc.contributor.author	Elizondo, Mónica G.
dc.contributor.author	Fonseca Casas, Pau
dc.date	2012
dc.identifier.citation	González, S. [et al.]. SIM-Randsharp: A hybrid algorithm for solving the ARC routing problem with stochastic demands. A: Winter Simulation Conference. "Proceedings of the 2012 Winter Simulation Conference". Berlín: 2012, p. 1-11.
dc.identifier.citation	978-1-4673-4781-5
dc.identifier.uri	http://hdl.handle.net/2117/17234
dc.language.iso	eng
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject	Àrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Programació matemàtica
dc.subject	Stochastic control theory
dc.subject	Processos estocàstics -- Models matemàtics
dc.subject	93E Sistemes estocàstics i control
dc.title	SIM-Randsharp: A hybrid algorithm for solving the ARC routing problem with stochastic demands
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	This paper proposes a new hybrid algorithm for solving the Arc Routing Problem with Stochastic Demands (ARPSD). Our approach combines Monte Carlo simulation (MCS) with the RandSHARP algorithm, which is designed for solving the Capacitated Arc Routing Problem (CARP) with deterministic demands. The RandSHARP algorithm makes use of a CARP-adapted version of the Clarke and Wright Savings heuristic, which was originally designed for the Vehicle Routing Problem. The RandSHARP algorithm also integrates a biased-randomized process, which allows it to obtain competitive results for the CARP in low computational times. The RandSHARP algorithm is then combined with MCS to solve the ARPSD. Some numerical experiments contribute to illustrate the potential benefits of our approach.
dc.description.abstract	Peer Reviewed

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