000063374 001__ 63374
000063374 005__ 20200221144210.0
000063374 0247_ $$2doi$$a10.1016/j.eswa.2016.05.028
000063374 0248_ $$2sideral$$a95256
000063374 037__ $$aART-2016-95256
000063374 041__ $$aeng
000063374 100__ $$0(orcid)0000-0003-1044-7335$$aFogue, Manuel$$uUniversidad de Zaragoza
000063374 245__ $$aNon-emergency patient transport services planning through genetic algorithms
000063374 260__ $$c2016
000063374 5060_ $$aAccess copy available to the general public$$fUnrestricted
000063374 5203_ $$aNon-emergency Patient Transport Services (PTS) are provided by ambulance companies for patients who do not require urgent and emergency transport. These patients require transport to or from a health facility like a hospital, but due to clinical requirements are unable to use private or public transport. This task is performed nowadays mainly by human operators, spending a high amount of time and resources to obtain solutions that are suboptimal in most cases. To overcome this limitation, in this paper we present NURA (Non-Urgent transport Routing Algorithm), a novel algorithm aimed at ambulance route planning. In particular, NURA relies on a genetic algorithm to explore the solution space, and it includes a scheduling algorithm to generate detailed routes for ambulances. Experimental results show that NURA is able to outperform human experts in several real scenarios, reducing the time spent by patients in ambulances during non-emergency transportations, increasing ambulance usage, while saving time and money for ambulance companies.
000063374 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T91$$9info:eu-repo/grantAgreement/ES/MINECO/TEC2014-52690-R
000063374 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000063374 590__ $$a3.928$$b2016
000063374 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b18 / 133 = 0.135$$c2016$$dQ1$$eT1
000063374 591__ $$aOPERATIONS RESEARCH & MANAGEMENT SCIENCE$$b3 / 83 = 0.036$$c2016$$dQ1$$eT1
000063374 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b37 / 260 = 0.142$$c2016$$dQ1$$eT1
000063374 592__ $$a1.343$$b2016
000063374 593__ $$aArtificial Intelligence$$c2016$$dQ1
000063374 593__ $$aEngineering (miscellaneous)$$c2016$$dQ1
000063374 593__ $$aComputer Science Applications$$c2016$$dQ1
000063374 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/submittedVersion
000063374 700__ $$0(orcid)0000-0001-7657-0075$$aSanguesa, Julio A.
000063374 700__ $$0(orcid)0000-0001-5664-6589$$aNaranjo, Fernando$$uUniversidad de Zaragoza
000063374 700__ $$0(orcid)0000-0001-9895-0837$$aGallardo, Jesús$$uUniversidad de Zaragoza
000063374 700__ $$0(orcid)0000-0002-1750-7225$$aGarrido, Piedad$$uUniversidad de Zaragoza
000063374 700__ $$0(orcid)0000-0001-6945-7330$$aMartinez, Francisco J.$$uUniversidad de Zaragoza
000063374 7102_ $$15007$$2035$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Arquit.Tecnología Comput.
000063374 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000063374 773__ $$g61 (2016), 262-271$$pExpert syst. appl.$$tExpert Systems with Applications$$x0957-4174
000063374 8564_ $$s586602$$uhttps://zaguan.unizar.es/record/63374/files/texto_completo.pdf$$yPreprint
000063374 8564_ $$s50690$$uhttps://zaguan.unizar.es/record/63374/files/texto_completo.jpg?subformat=icon$$xicon$$yPreprint
000063374 909CO $$ooai:zaguan.unizar.es:63374$$particulos$$pdriver
000063374 951__ $$a2020-02-21-13:11:05
000063374 980__ $$aARTICLE