Open Access

ARTICLE

Network Analysis for Projects with High Risk Levels in Uncertain Environments

by Mohamed Abdel-Basset¹, Asmaa Atef¹, Mohamed Abouhawwash^2,3, Yunyoung Nam^4,*, Nabil M. AbdelAziz¹

1 Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519, Egypt
2 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
3 Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, 48824, MI, USA
4 Department of Computer Science and Engineering, Soonchunhyang University, Asan, 31538, Korea

* Corresponding Author: Yunyoung Nam. Email:

Computers, Materials & Continua 2022, 70(1), 1281-1296. https://doi.org/10.32604/cmc.2022.018947

Received 26 March 2021; Accepted 27 April 2021; Issue published 07 September 2021

Abstract

The critical path method is one of the oldest and most important techniques used for planning and scheduling projects. The main objective of project management science is to determine the critical path through a network representation of projects. The critical path through a network can be determined by many algorithms and is useful for managing, monitoring, and controlling the time and cost of an entire project. The essential problem in this case is that activity durations are uncertain; time presents considerable uncertainty because the time of an activity is not always easily or accurately estimated. This issue increases the need to use neutrosophic theory to solve the critical path problem. Real-world problems are characterized by a lack of precision, consistency, and completeness. The concept of neutrosophic sets has been introduced as a generalization of fuzzy, intuitionistic fuzzy, and crisp sets to overcome the ambiguity surrounding real-world problems. Truth-, falsity-, and indeterminacy-membership functions are used to express neutrosophic elements. This study was performed to examine a neutrosophic event-oriented algorithm for determining the critical path in activity-on-arc networks. The activity time estimates are presented as trapezoidal neutrosophic numbers, and score and accuracy functions are used to obtain a crisp model of the problem. An appropriate numerical example is then used to explain the proposed method.

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Keywords

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