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Project Management Monitoring Based on Expected Duration Entropy

Projects are rarely executed exactly as planned. Often, the actual duration of a project’s activities differ from the planned duration, resulting in costs stemming from the inaccurate estimation of the activity’s completion date. While monitoring a project at various inspection points is pricy, it c...

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Detalles Bibliográficos
Autores principales: Cohen Kashi, Shiva, Rozenes, Shai, Ben-Gal, Irad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517532/
https://www.ncbi.nlm.nih.gov/pubmed/33286674
http://dx.doi.org/10.3390/e22080905
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author Cohen Kashi, Shiva
Rozenes, Shai
Ben-Gal, Irad
author_facet Cohen Kashi, Shiva
Rozenes, Shai
Ben-Gal, Irad
author_sort Cohen Kashi, Shiva
collection PubMed
description Projects are rarely executed exactly as planned. Often, the actual duration of a project’s activities differ from the planned duration, resulting in costs stemming from the inaccurate estimation of the activity’s completion date. While monitoring a project at various inspection points is pricy, it can lead to a better estimation of the project completion time, hence saving costs. Nonetheless, identifying the optimal inspection points is a difficult task, as it requires evaluating a large number of the project’s path options, even for small-scale projects. This paper proposes an analytical method for identifying the optimal project inspection points by using information theory measures. We search for monitoring (inspection) points that can maximize the information about the project’s estimated duration or completion time. The proposed methodology is based on a simulation-optimization scheme using a Monte Carlo engine that simulates potential activities’ durations. An exhaustive search is performed of all possible monitoring points to find those with the highest expected information gain on the project duration. The proposed algorithm’s complexity is little affected by the number of activities, and the algorithm can address large projects with hundreds or thousands of activities. Numerical experimentation and an analysis of various parameters are presented.
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spelling pubmed-75175322020-11-09 Project Management Monitoring Based on Expected Duration Entropy Cohen Kashi, Shiva Rozenes, Shai Ben-Gal, Irad Entropy (Basel) Article Projects are rarely executed exactly as planned. Often, the actual duration of a project’s activities differ from the planned duration, resulting in costs stemming from the inaccurate estimation of the activity’s completion date. While monitoring a project at various inspection points is pricy, it can lead to a better estimation of the project completion time, hence saving costs. Nonetheless, identifying the optimal inspection points is a difficult task, as it requires evaluating a large number of the project’s path options, even for small-scale projects. This paper proposes an analytical method for identifying the optimal project inspection points by using information theory measures. We search for monitoring (inspection) points that can maximize the information about the project’s estimated duration or completion time. The proposed methodology is based on a simulation-optimization scheme using a Monte Carlo engine that simulates potential activities’ durations. An exhaustive search is performed of all possible monitoring points to find those with the highest expected information gain on the project duration. The proposed algorithm’s complexity is little affected by the number of activities, and the algorithm can address large projects with hundreds or thousands of activities. Numerical experimentation and an analysis of various parameters are presented. MDPI 2020-08-18 /pmc/articles/PMC7517532/ /pubmed/33286674 http://dx.doi.org/10.3390/e22080905 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cohen Kashi, Shiva
Rozenes, Shai
Ben-Gal, Irad
Project Management Monitoring Based on Expected Duration Entropy
title Project Management Monitoring Based on Expected Duration Entropy
title_full Project Management Monitoring Based on Expected Duration Entropy
title_fullStr Project Management Monitoring Based on Expected Duration Entropy
title_full_unstemmed Project Management Monitoring Based on Expected Duration Entropy
title_short Project Management Monitoring Based on Expected Duration Entropy
title_sort project management monitoring based on expected duration entropy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517532/
https://www.ncbi.nlm.nih.gov/pubmed/33286674
http://dx.doi.org/10.3390/e22080905
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