A macro approach to modeling projects with uncertain network structures.

A macro approach to modeling projects with uncertain network structures.

This paper presents an approach for modeling and analyzing project uncertainty at the network, rather than at the activity, level. This approach is applicable for project schedule risk analysis and contingency planning. The suggested approach requires that a set of project network scenarios he able...

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Bibliographic Details
Main Authors: Liberatore, Matthew., Pollack-Johnson, Bruce.
Format: Villanova Faculty Authorship
Language:English
Published: 2003
Online Access:http://ezproxy.villanova.edu/login?url=https://digital.library.villanova.edu/Item/vudl:178065
Description
Summary:This paper presents an approach for modeling and analyzing project uncertainty at the network, rather than at the activity, level. This approach is applicable for project schedule risk analysis and contingency planning. The suggested approach requires that a set of project network scenarios he able to he identified, each with an assessed probability of occurrence. These scenarios might differ according to the results . of uncertain events that could occur during the course of the project, uncertain activity durations, finite loops, or a combination of these. In this paper we present a general approach for modeling and analyzing the set of network scenarios. An advantage of our approach is that it uses standard methods, such as critical path analysis and probability analysis, to solve project planning problems with uncertain network structures. Our approach also leads io the development of new project network uncertainty measures, including expected and conditional activity criticality and slack, and early and late start and finish times for repeated activities resulting from looping. A second henelit is greater accessihility and likelihood of the use of uncertainty analysis in project planning, since the data needs and the analysis are focused on the key scenarios driving schedule uncertainty. Several examples are presented to illustrate the proposed approach, including random events, loops, and random activity times. Suggestions for future research include field testing the proposed approach and determining the conditions under which it is preferable to simulation.

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