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Open AccessFeature PaperArticle

Project Management Monitoring Based on Expected Duration Entropy

Department of Industrial Engineering, Tel-Aviv University, Tel-Aviv 6997801, Israel
School of Industrial Engineering and Management, Afeka College of Engineering, Tel Aviv 6910717, Israel
Author to whom correspondence should be addressed.
Entropy 2020, 22(8), 905;
Received: 28 June 2020 / Revised: 5 August 2020 / Accepted: 14 August 2020 / Published: 18 August 2020
(This article belongs to the Special Issue Applications of Information Theory to Industrial and Service Systems)
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. View Full-Text
Keywords: project management; information theory; uncertainty project management; information theory; uncertainty
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MDPI and ACS Style

Cohen Kashi, S.; Rozenes, S.; Ben-Gal, I. Project Management Monitoring Based on Expected Duration Entropy. Entropy 2020, 22, 905.

AMA Style

Cohen Kashi S, Rozenes S, Ben-Gal I. Project Management Monitoring Based on Expected Duration Entropy. Entropy. 2020; 22(8):905.

Chicago/Turabian Style

Cohen Kashi, Shiva; Rozenes, Shai; Ben-Gal, Irad. 2020. "Project Management Monitoring Based on Expected Duration Entropy" Entropy 22, no. 8: 905.

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