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    Fitting activity distributions using human partitioning and statistical calibration

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    Publication type
    Journal article with impact factor
    Author
    Vanhoucke, Mario
    Batselier, Jordy
    Publication Year
    2019
    Journal
    Computers and Industrial Engineering
    Publication Volume
    129
    Publication Issue
    March
    Publication Begin page
    126
    Publication End page
    135
    
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    Abstract
    Many project management and scheduling studies have modelled activity durations as a range of values to express the stochastic nature of projects in progress. A wide variety of simulation models have been proposed that all rely on pre-defined statistical probability distributions for the durations of project activities. Ideally, these distributions reflect the real stochastic nature of the activities to assure that the simulations imitate the expected reality in the best possible way. However, the distributions are often selected ad hoc, relying on a class of distributions that are often used in the statistical literature, but without having much links with the features of real projects. Recently, a calibration method has been proposed in literature and validated on a set of 24 projects that makes use of real project data to derive realistic statistical distributions. This paper builds further on the validation of this calibration method in three different ways. First, the procedure is now successfully used on a set of 125 projects (for which 83 could be used for the final analysis) from different sectors. Secondly, the procedure has been extended with a partitioning step performed by humans with experience in the particular project. Finally, some procedural extensions have been proposed to test the necessity of each step of the procedure.
    Keyword
    Project Management, Empirical Data, Activity Durations, Distribution Fitting, Parkinson Distribution, Lognormal Distribution, Project Partitioning, Managerial Criteria, Risk Profiles
    Knowledge Domain/Industry
    Operations & Supply Chain Management
    DOI
    0360-8352
    URI
    http://hdl.handle.net/20.500.12127/6600
    ae974a485f413a2113503eed53cd6c53
    0360-8352
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