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Using Fuzzy Expert System for Performance Evaluation and Decision Making in Project-Based Companies

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Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 43))

Abstract

It is well known that business performance can be improved with effective knowledge management, especially with today’s competitive atmosphere. Thus, a proper performance measurement and evaluation system supports the decision makers to measure progress, identify assets of improvement, and find unidentified difficulties within the company. Accordingly, study about utilisation of expert systems like fuzzy logic and understanding their importance is worthy; to support the performance evaluation and decision-making processes in companies. Therefore, a comparative study has been practiced for this research, through reviewing existing papers and mechanisms in expert system fields. A conceptual framework is then introduced; to demonstrate the idea of using the fuzzy expert system for performance evaluation and decision making in project-based companies. Finally, this paper presents a fuzzy model integrated with other methods BSC, AHP and MCDM (TOPSIS), to measure the performance, evaluate the performance and rank them as per performance results in project-based companies by using MATLAB.

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References

  1. Azadeh A, Fam IM, Khoshnoud M, Nikafrouz M (2008) Design and implementation of a fuzzy expert system for performance assessment of an integrated health, safety, environment (HSE) and ergonomics system: the case of a gas refinery. Inf Sci 178(22):4280–4300

    Article  Google Scholar 

  2. Baccarini D (1999) The logical framework method for defining project success. Project Manag J 30(4):25–32

    Article  Google Scholar 

  3. Carr V, Tah JHM (2001) A fuzzy approach to construction project risk assessment and analysis: construction project risk management system. Adv Eng Softw 32(10-11):847–857

    Article  Google Scholar 

  4. Cheung WW, Pitcher TJ, Pauly D (2007) Using an expert system to valuate vulnerabilities and conservation risk of marine fishes from fishing. New research on expert system. Nova Science Publishers, New York

    Google Scholar 

  5. Dweiri FT, Kablan MM (2006) Using fuzzy decision making for the evaluation of the project management internal efficiency. Decis Support Syst 42(2):712–726

    Article  Google Scholar 

  6. Ertuğrul İ, Karakaşoğlu N (2009) Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Syst Appl 36(1):702–715

    Article  Google Scholar 

  7. Hayward G, Davidson V (2003) Fuzzy logic applications. Analyst 128(11):1304–1306

    Article  Google Scholar 

  8. https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_fuzzy_logic_systems.htm

  9. Jamshidi M, Titli A, Zadeh L, Boverie S (1997) Applications of fuzzy logic: towards high machine intelligence quotient systems. Prentice-Hall, Inc.

    Google Scholar 

  10. Lee AH, Chen WC, Chang CJ (2008) A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan. Expert Syst Appl 34(1):96–107

    Article  Google Scholar 

  11. Li J, Huang GH, Zeng G, Maqsood I, Huang Y (2007) An integrated fuzzy-stochastic modeling approach for risk assessment of groundwater contamination. J Env Manage 82(2):173–188

    Article  Google Scholar 

  12. Liao SH (2005) Expert system methodologies and applications—a decade review from 1995 to 2004. Expert Syst Appl 28(1):93–103

    Article  Google Scholar 

  13. Lin C, Hsieh PJ (2004) A fuzzy decision support system for strategic portfolio management. Decis Support Syst 38(3):383–398

    Article  Google Scholar 

  14. Marques G, Gourc D, Lauras M (2011) Multi-criteria performance analysis for decision making in project management. Int J Project Manag 29(8):1057–1069

    Article  Google Scholar 

  15. Mendel JM (1995) Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE 83(3):345–377

    Article  Google Scholar 

  16. Negnevitsky M (2011) Artificial intelligence: a guide to intelligent systems. Pearson Education Limited

    Google Scholar 

  17. PMI (2013) A guide to the project management body of knowledge (PMBOK guide). Fifth Edition, Project Management Institute

    Google Scholar 

  18. Pourjavad E, Mayorga RV (2017) A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system. J Intell Manuf, pp. 1–13

    Google Scholar 

  19. Qureshi TM et al (2008) Significance of project management performance assessment (PMPA) model. Int J Project Manage

    Google Scholar 

  20. Siler W, Buckley JJ (2005) Fuzzy expert systems and fuzzy reasoning. Wiley & Sons Inc, Hoboken, New Jersey

    MATH  Google Scholar 

  21. Sun CC (2010) A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Syst Appl 37:7745–7754

    Article  Google Scholar 

  22. Walk K (1998) How to write a comparative analysis? Writing Center at Harvard University.

    Google Scholar 

  23. Wu HY, Tzeng GH, Chen YH (2009) A fuzzy MCDM approach for evaluating banking performance based on balanced scorecard. Expert Syst Appl 36(6):10135–10147

    Article  Google Scholar 

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Correspondence to Khalid Al Marri .

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Almaazmi, J., Al Marri, K. (2020). Using Fuzzy Expert System for Performance Evaluation and Decision Making in Project-Based Companies. In: Abu-Tair, A., Lahrech, A., Al Marri, K., Abu-Hijleh, B. (eds) Proceedings of the II International Triple Helix Summit. THS 2018. Lecture Notes in Civil Engineering, vol 43. Springer, Cham. https://doi.org/10.1007/978-3-030-23898-8_16

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  • DOI: https://doi.org/10.1007/978-3-030-23898-8_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23897-1

  • Online ISBN: 978-3-030-23898-8

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