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            <title xml:lang="en">A Bounding Technique for Probabilistic PERT</title>
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                <forename type="first">Maksim</forename>
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            <idno type="halRefHtml">&lt;i&gt;13th International Conference on Research and Practical Issues of Enterprise Information Systems (CONFENIS)&lt;/i&gt;, Dec 2019, Prague, Czech Republic. pp.112-122, &lt;a target="_blank" href="https://dx.doi.org/10.1007/978-3-030-37632-1_10"&gt;&amp;#x27E8;10.1007/978-3-030-37632-1_10&amp;#x27E9;&lt;/a&gt;</idno>
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                <title xml:lang="en">A Bounding Technique for Probabilistic PERT</title>
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                    <forename type="first">Maksim</forename>
                    <surname>Goman</surname>
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                  <title>13th International Conference on Research and Practical Issues of Enterprise Information Systems (CONFENIS)</title>
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                  <date type="end">2019-12-17</date>
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                <editor>Petr Doucek</editor>
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                <editor>A Min Tjoa</editor>
                <editor>Maria Raffai</editor>
                <editor>Antonin Pavlicek</editor>
                <editor>Katrin Detter</editor>
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                <term xml:lang="en">Path duration</term>
                <term xml:lang="en">Project management</term>
                <term xml:lang="en">Time bounds</term>
                <term xml:lang="en">Bounding technique</term>
                <term xml:lang="en">Stochastic PERT</term>
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              <p>Bounding time distributions has been an effective way of improvements of the original PERT method. Analytical enhancement of PERT is often an effective time bounding approach. However, one thing that is missing today is a combination of time distributions which parameters can be effectively obtained empirically and the effective bounding technique for them. We aim at addressing this gap and suggest Cornish-Fisher expansion (CFE) to compute time bounds in formal models like the classical PERT.We argue that CFE allows us to evaluate analytically approximate time bounds easily without resort to simulations. This bounding approach is useful in case of complex distribution functions of task durations, because analytical derivation of project completion time distribution is tedious. Our example shows CFE usage for uniform time distributions and comparison with time bounds of classical PERT.</p>
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