<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-06-18T00:46:07Z</responseDate><request verb="GetRecord" identifier="oai:riubu.ubu.es:10259/4213" metadataPrefix="etdms">https://riubu.ubu.es/oai/request</request><GetRecord><record><header><identifier>oai:riubu.ubu.es:10259/4213</identifier><datestamp>2021-11-02T12:04:25Z</datestamp><setSpec>com_10259_3830</setSpec><setSpec>com_10259_5086</setSpec><setSpec>com_10259_2604</setSpec><setSpec>col_10259_4210</setSpec></header><metadata><thesis xmlns="http://www.ndltd.org/standards/metadata/etdms/1.0/" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.ndltd.org/standards/metadata/etdms/1.0/ http://www.ndltd.org/standards/metadata/etdms/1.0/etdms.xsd">
<title>Exploring the influence of seasonal uncertainty in project risk management</title>
<creator>Acebes, Fernando</creator>
<creator>Pajares Gutiérrez, Javier</creator>
<creator>Galán Ordax, José Manuel</creator>
<creator>López Paredes, Adolfo</creator>
<subject>Uncertainty and variability</subject>
<subject>Risk Management</subject>
<subject>Monte Carlo Simulation</subject>
<subject>Criticality</subject>
<subject>Schedule Risk Baseline</subject>
<description>27th IPMA World Congress</description>
<description>For years, many research studies have focused on programming projects, assuming a deterministic environment and complete&#xd;
task information. However, during the project performance, schedule may be subject to uncertainty which can lead to&#xd;
significant modifications. This fact has led to an increasing scientific literature in the field. In this article we consider the&#xd;
presence of an uncertainty of seasonal type (e.g. meteorological) that affects some of the activities that comprise the project. We&#xd;
discuss how the project risk can be affected by such uncertainty, depending on the start date of the project. By means of Monte&#xd;
Carlo simulation, we compute the statistical distribution functions of project duration at the end of the project. Then, we&#xd;
represent the variability of the project through the so-called Project Risk Baseline.&#xd;
In addition, we examine various sensitivity metrics - Criticality, Cruciality, Schedule Sensitivity Index -. We use them to&#xd;
prioritize each one of the activities of the project depending on its start date. In the last part of the study we demonstrate the&#xd;
relative importance of project tasks must consider a combined version of these three sensitivity measures.</description>
<date>2016-08-31</date>
<date>2016-08-31</date>
<date>2014-03</date>
<type>info:eu-repo/semantics/conferenceObject</type>
<identifier>1877-0428</identifier>
<identifier>http://hdl.handle.net/10259/4213</identifier>
<identifier>10.1016/j.sbspro.2014.03.038</identifier>
<language>eng</language>
<relation>Procedia - Social and Behavioral Sciences. 2014, V. 119, p. 329-338</relation>
<relation>http://dx.doi.org/10.1016/j.sbspro.2014.03.038</relation>
<relation>info:eu-repo/grantAgreement/JCyL/VA056A12-2</relation>
<rights>http://creativecommons.org/licenses/by-nc-nd/3.0</rights>
<rights>info:eu-repo/semantics/openAccess</rights>
<rights>Attribution-NonCommercial-NoDerivs 3.0 Unported</rights>
<publisher>Elsevier</publisher>
</thesis></metadata></record></GetRecord></OAI-PMH>