Artificial Intelligence Techniques Used in Project Management

Year 2021, Volume: 1 Issue: 1, 1 - 5, 15.01.2021

Çetin Elmas , Jahid Babayev

Abstract

Artificial intelligence is defined as the ability of a machine to mimic intelligent human behaviour and therefore attempts to simulate human cognition capability through symbol manipulation and symbolically structured knowledge bases. Because of many uncertain factors, complicated influence factors in project management, every project has its individual character and generality. Over the last decade, the development and application of artificial intelligence in project management has given good grounds for expecting of achievements, which are mainly used in project evaluation, diagnosis, decision-making and prediction. This paper presents artificial intelligence techniques used in project management. It tries to gather the recent advances and trends on artificial intelligence techniques used in Project Management.

Keywords

Artificial intelligence, Project management, Expert systems, Genetic algorithms, Artificial neural network

References

• Elmas, Ç., Application of Artificial Intelligence, 4. Edition, Seçkin Publications, 2018
• Zadeh, L. A. (1996). "Fuzzy logic = computing with words". IEEE Transactions on Fuzzy Systems. 4 (2): 103–111. doi:10.1109/91.493904
• Elmas, Ç., and Elmas, A., Modern Project Management, 4. Edition, Seçkin Publications, 2018
• Discenza, R. & Forman, J. B. (2007). Seven causes of project failure: how to recognize them and how to initiate project recovery. Paper presented at PMI® Global Congress 2007—North America, Atlanta, GA. Newtown Square, PA: Project Management Institute.
• Traylor, R. C., Stinson, R. C., Madsen, J. L., Bell, R. S., & Brown, K. R. (1984). Project management under uncertainty: network paths and completion time. Project Management Journal, 15(1), 66–75.
• https://www.liquidplanner.com/blog/stats-2017-project-management-manufacturing-report/
• https://www.pmi.org/-/media/pmi/documents/public/pdf/learning/thought-leadership/pulse/the-essential-role-of-communications.pdf
• https://www.prnewswire.com/news-releases/up-to-75-of-business-and-it-executives-anticipate-their-software-projects-will-fail-117977879.html
• Johnsonbabu, A., Reinventing the role of Project manager in the Artificial intelligence era, Project Management National Conference, India, 15-17 September, 2017
• ISO 21500. (2012). Guidance on Project Management. Geneva: International Organization for Standardization, 2012.
• Waziri, B. S., Bala, K. and Bustani, S. A. (2017). Artificial Neural Networks in Construction Engineering and Management. International Journal of Architecture, Engineering and Construction, 6(1), 50-60. Doi: 10.7492/IJAEC.2017.006
• Ha, L. H., Hung, L., & Trung, L. Q. (2018). A risk assessment framework for construction project using artificial neural network. Journal of Science and Technology in Civil Engineering (STCE) - NUCE, 12(5), 51-62. https://doi.org/10.31814/stce.nuce2018-12(5)-06
• Celani de Souza, H. J., Salomon, V., Silva, C. E. S., Campos de Aguiar, D. (2012), Project Management Maturity: an Analysis with Fuzzy Expert Systems, Brazilian Journal of Operations & Production Management, Volume 9, Number 1, 2012, pp. 29-41, http://dx.doi.org/10.4322/bjopm.2013.003
• Mazlum, M., Güneri, A. F., (2015), CPM, PERT and Project Management with Fuzzy Logic Technique and Implementation on a Business Procedia - Social and Behavioral Sciences, Volume 210, Pages 348-357. https://doi.org/10.1016/j.sbspro.2015.11.378
• Zgurowsky, M. Z., Kovalenko, I. I., Kondrak, K., Kondrak, E. (2001), Expert Systems in Project Management, Journal of Automation and Information Sciences 33(1):7. DOI: 10.1615/JAutomatInfScien.v33.i1.110
• Liao S-H (2005) Expert system methodologies and applications—a decade review from 1995 to 2004. Expert Syst Appl 28:93–103
• Carl K. C., Christensen, M. J., Tao Z. (2001), Genetic Algorithms for Project Management. Annals of Software Engineering 11(1):107-139. DOI: 10.1023/A:1012543203763
• Kuehn, M., Zahid, T., Voelker, M., Zhou, Z., Rose, O. (2016). Investigation Of Genetic Operators And Priority Heuristics for Simulation Based Optimization Of Multi-Mode Resource Constrained Multi-Project Scheduling Problems (MMRCMPSP), ECMS 2016 Proceedings, 30th European Conference on Modelling and Simulation, Regensburg Germany. doi:10.7148/2016-0481