{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T10:04:59Z","timestamp":1773396299483,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T00:00:00Z","timestamp":1597708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Projects are rarely executed exactly as planned. Often, the actual duration of a project\u2019s activities differ from the planned duration, resulting in costs stemming from the inaccurate estimation of the activity\u2019s 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\u2019s 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\u2019s estimated duration or completion time. The proposed methodology is based on a simulation-optimization scheme using a Monte Carlo engine that simulates potential activities\u2019 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/e22080905","type":"journal-article","created":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T11:15:27Z","timestamp":1597749327000},"page":"905","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Project Management Monitoring Based on Expected Duration Entropy"],"prefix":"10.3390","volume":"22","author":[{"given":"Shiva","family":"Cohen Kashi","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Tel-Aviv University, Tel-Aviv 6997801, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shai","family":"Rozenes","sequence":"additional","affiliation":[{"name":"School of Industrial Engineering and Management, Afeka College of Engineering, Tel Aviv 6910717, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2411-5518","authenticated-orcid":false,"given":"Irad","family":"Ben-Gal","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Tel-Aviv University, Tel-Aviv 6997801, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,18]]},"reference":[{"key":"ref_1","unstructured":"Project Management Institute (2017). 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