Sage Journals: Discover world-class research

Abstract

Management issue incorporates a huge amount of fuzzy information processing, that is exactly representing the essentials of human intelligence in reasoning for managing problems. This paper distinguishes the qualitative (empirical) management versus the quantitative (digital) management and focuses the studies of the latter one and verifies the possibility and necessity of adopting quantitative project management in many real world problems. The theoretical arguments of “Fuzzy-AI model” are presented for dealing with the quantization of uncertain management events, which simulates the intelligence of human being by AI (Artificial Intelligence) technology and quantizes the management solution by fuzzy approach. Several practical examples are presented in this paper as the evidence of the applicability of this model in the project management issues, which provides digital support to decision maker in the management. Finally, the theoretical framework of applying this model in different stages of project management is presented for the purpose of calibrating its application scope.

Keywords

Fuzzy-AI Model quantitative management project management theoretical framework performance matrix

Get full access to this article

View all access options for this article.

References

Chen

H.Y.

and Lin

S.P.

, Studies of intelligent decision supporting system for real estate, Proceedings of 6-th National Conference on Computer Applications in Civil Engineering, 1995, pp. 43–50. (in Chinese).

Lin

S.P.

and Wu

W.R.

, On human factors affected to cooperative project with people of different cultural background, Proceedings of the 3rd International Symposium on Human Factors in Organizational Design and Management, Kyoto, Japan, 1990.

Lin

S.P.

, et al., Fuzzy economic assessment for offshore oil fields, Journal of China Ocean Engineering 4(1) (1990), 97–108.

Lin

S.P.

, et al., A conceptual approach of fuzzy decision for systems by “deep knowledge” and “deep data”, Proceedings of 2nd International Conference on the Applications of AI Technology in Civil and Structural Engineering, UK, 1991.

Lin

S.P.

, et al., Fuzzy reasoning and machine learning of expert system for structural design, Computational Mechanics in Structural Engineering, Elsevier Applied Science, 1992, pp. 450–463.

Lin

S.P.

, Fuzzy-AI Model, Proceedings of 2nd International Conference on Artificial Intelligence for Engineering, Wuhan, China, 1998a, pp. 56–72.

Lin

S.P.

, Fuzzy modeling of investment decision in engineering project, Uncertainty Modeling and Analysis in Civil Engineering, CRC press, USA, 1998b, pp. 167–188.

Lin

S.P.

, On paradox of Fuzzy-AI modeling: Supervised learning for rectifying fuzzy membership functions, Journal of Artificial Intelligence Review 23 (2005), 395–405.

Lin

S.P.

, Fuzzy-AI model for managerial science, Keynote Plenary Speech of the 3rd PMI World Research Conference, Warsaw, Poland, 2008.

10.

Lin

S.P.

, et al., The Fuzzy-AI Modeling for Optimization of Long-Term Metro Vehicle Repair, Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) 2009 vol. 6, Tianjin, China, 2009, pp. 459–463.

11.

Lin

S.P.

, et al., RCM-Fuzzy Model and its Applications to Maintenance with Cost/Serviceability Optimization, Proceedings of the PACIIA Conference 2010, Wuhan, China, 2010, pp. 4–6.

12.

Lin

S.P.

and Zhang

W.Y.

, Decision traps and fuzzy quantitative risk management of overseas projects, Civil Engineering Innovation 5(1) (2011), 29–40.

13.

Lin

S.P.

, et al., Optimization Studies of Metro repair based on “FEMA-Fuzzy” Model (I) — Theoretical Studies, Journal of Optimization in Infrastructure Management 24(2) (2012a), 22–28. (in Chinese).

14.

Lin

S.P.

, et al., Optimization Studies of Metro repair based on “FEMA-Fuzzy” Model (II) — Software Implementation, Journal of Optimization in Infrastructure Management 24(3) (2012b), 14–19. (in Chinese).

15.

Lin

S.P.

, Principles and Applications of Quantitative Management — Fuzzy-AI Model in Managerial Science, Comprehensive PM Learner Co. Ltd., 2014.

16.

PMI, Project Management Body of Knowledge (PMI-PMBOK), PMI Press, 2013

17.

Saaty

T.L.

, The analytic hierarchy process, McGraw Hill, New York, 1980.

18.

Vasant

P.M.

, et al., Innovation in Power, Control, and Optimization: Emerging Energy Technologies, Hershey, PA, IGI Global, 2012, pp. 1–396.

19.

Vasant

P.M.

, Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance, Hershey, PA, IGI Global 2013, pp. 1–734.

20.

Vasant

P.M.

, Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications, Hershey, PA, IGI Global, 2014, pp. 1–1018.

21.

Vasant

P.M.

, Handbook of Research on Artificial Intelligence Techniques and Algorithms, Hershey, PA, IGI Global, 2015, pp. 1–873.

22.

and Lin

S.P.

, Financing risk analysis and its Fuzzy-AI model solution, Journal of Project management Technology 12(6) (2014a), 101–103. (in Chinese).

23.

and Lin

S.P.

, Risk management in overseas project construction and investment by Fuzzy-AI model, Journal of Optimization in Infra-Structure Investment 26(1) (2014b), 2–7. (in Chinese).

24.

, et al., Quantitative Solution of Overseas Project Risk Management by Knowledge Engineering, Proceedings of International Conference on Sustainable Development of Critical Infrastructure (IC-SDCI) 2014, Shanghai, China, 2014, pp. 16–18.

25.

Yang

X.F.

and Lin

S.P.

, Long-term highway system investment decision based on fuzzy model of comprehensive pavement distress, Proceedings of ISUMA’95, 1995.

26.

Zadeh

L.A.

, Fuzzy sets, Information and Control 8 (1965), 338–353.

Theoretical framework of Fuzzy-AI model in quantitative project management

Abstract

Keywords

Get full access to this article

References