Naturally, project managers are responsible for making the project finish on time, on budget, thus they always be evaluated on the same perspective. But due to the different content of each project, this could be inappropriate to judge their performance form the project performance.
Compare to the traditional studies, the proposed model developed under the more multi-dimensional perspective, the schedule basis cannot stand alone without the qualitative perspective. The qualitative perspective such as attitude or some specific behavior much is taken in to account in the evaluation.
By using fuzzy multi-criteria decision making methods, allow the model to evaluate both quantitative data (schedule basis, financial basis) and qualitative perspective. Subsequently, we proposed that the Project manager performance evaluation using fuzzy weight average based model can be used easily and more effective than the single-dimension evaluation method.

Naturally, project managers are responsible for making the project finish on time, on budget, thus they always be evaluated on the same perspective. But due to the different content of each project, this could be inappropriate to judge their performance form the project performance.
Compare to the traditional studies, the proposed model developed under the more multi-dimensional perspective, the schedule basis cannot stand alone without the qualitative perspective. The qualitative perspective such as attitude or some specific behavior much is taken in to account in the evaluation.
By using fuzzy multi-criteria decision making methods, allow the model to evaluate both quantitative data (schedule basis, financial basis) and qualitative perspective. Subsequently, we proposed that the Project manager performance evaluation using fuzzy weight average based model can be used easily and more effective than the single-dimension evaluation method.

ACKNOWLEDGEMENTS I
ABSTRACT II
TABLE OF CONTENTS III
LIST OF TABLES VI
LIST OF FIGURES VII
CHAPTER ONE – INTRODUCTION 1
1.1 Background and Motivation 1
1.2 Research Objectives 2
1.3 Research Limitations 2
1.4 Research Framework 2
CHAPTER TWO - LITERATURE REVIEW 4
2.1 Project Management 4
2.2 Project Success 5
2.3 Project Manager 5
2.4 Financial Measures 6
2.4.1 Cost, Schedule and Performance 6
2.4.2 Net Present Value 6
2.4.3 Earn Value Management System 7
2.5 Non-financial Performance Measures 7
2.5.1 Balanced Score Card 8
2.5.2 Leadership Behavior and Managerial Practice 8
2.5.3 Project Stability 9
2.6 Criteria Selection 11
2.7 Fuzzy Weighted Average 11
2.8 Fuzzy Number Ranking 13
CHAPTER THREE - FUZZY SET THEORY 16
3.1 Fuzzy Set 16
3.2 Fuzzy Number 16
3.3 α-cut and Interval Arithmetic 18
3.4 Linguistic Values and Fuzzy Numbers 19
3.5 Defuzzification 20
CHAPTER FOUR - FUZZY WEIGHTED AVERAGE ARITHMETIC MODEL 21
4.1 Determine the Alternatives and Criteria 21
4.2 Aggregate the Importance Weights 21
4.3 Aggregate the Rating of Alternative versus Criteria 22
4.3.1 To Evaluate the Alternatives versus Qualitative Criteria 22
4.3.2 To Evaluate the Alternatives versus Quantitative Criteria 23
4.4 Develop Membership Function for FWA using Interval Analysis and
α-cut 24
4.4.1 Making Final Fuzzy Equation for Final Fuzzy Value, T_i 24
4.4.2 Ranking Obtain 26
4.5 Additional phase: The Explanation of Normalization Method 34
CHAPTER FIVE - NUMERICAL EXAMPLE 39
5.1 Determine the Alternatives and Criteria 39
5.2 Aggregate the Importance Weights 40
5.3 Aggregate the Ratings of Alternatives versus Criteria 41
5.3.1 Aggregate the Ratings of Alternatives versus Qualitative Criteria 42
5.3.2 Ratings of Alternative versus Quantitative Criteria 45
5.3.3 Normalize the Average Rating 46
5.4 Develop Inverse Membership Function FWA Using Internal Analysis and α-cut 47
5.5 Obtain Ranking Values 47
5.6 Rank the Project Managers according to the Final Fuzzy Ranking Values 48
CHAPTER SIX - CONCLUSIONS 49
REFERENCE 51
APPENDIX 55
Appendix Detail Calculation Illustration for Numerical Example 55

Abbasbandy, S., & Hajjari, T. (2009). A new approach for ranking of trapezoidal fuzzy numbers. Computers and Mathematics with Applications, 57, 413–419.
Belassi, W., Tukel, O., (1996). A new framework for determining critical success/failure factors in projects. Int. J. Project Manage, 14 (3), 141–151.
Blanchard, F. (1995). Net present value: An old tool finding use in assessing public sector capital expenditures. Project Management Journal, 26(1), 41–47.
Blomquist T, & Müller R. (2006). Middle managers in program and portfolio management: practice, roles and responsibilities. Newton Square (USA): Project Management Institute.
Chen, S. J., Hwang, C. L. and Hwang, F. P. (1992). Fuzzy Multiple Attribute Decision Making Methods and Applications. Berlin, Germany: Springer-Verlag.
Chen, S.J. & Hwang, C.L. (1992). Fuzzy Multiple Attribute Decision Making,Springer-Verlag, Berlin.
Chen, S. H., & Lee, H. T. (2007). Performance evaluation model for project managers using managerial practices. International Journal of Project Management, 25, 543–551.
Chen, Chiu W. & Yang, T. (2009). An incentive pay system for project management based on responsibility assignment matrix and fuzzy linguistic variables. Eepert Systems with Applications, 36, 12585-12591.
Chu T.CH, Yichen L. (2008). An extension to fuzzy MCDM. Computers and Mathematics with Applications, 57, 445-454.

Chu, T., Tsao C. (2002). Ranking fuzzy numbers with an area between the centroid point and orginal point, Comput. Math. Appl. 43 11_117.
Christensen, D. S. (1994). A review of cost/schedule control system criteria literature. Project Management Journal, 25(3), 32–41.
Cooke-Davies, T. (2001). The ‘‘real’’ project success factors. Int J Project Manage, 20(3), 185–90.
Cooke-Davies, T. (2002). The ‘real’ success factors on projects. International Journal of Project Management 20, 185–190.
Dubois, D. & Prade, H. (1987). The mean value of a fuzzy number. Fuzzy Sets and Systems, 24, 279-300.
Dulewicz, V., & Higgs MJ. (2003) Design of a new instrument to assess leadership dimensions and styles. UK: Henley Management College.
Dulewicz, V., & Young, M. (2006). Leadership styles, change context and leader performance in the Royal Navy. Journal of Change Management,6(4),383–396.
Dweiri, F.T., & Kablan, M.M. Using fuzzy decision making for evaluation of the projectmanagement internal efficiency. Decision Support Sytems, 42, 712-726.
Garrett, G. A. (2006). Earned value management. Contract Management, 46(12), 49–51.
Grant, K. P., & Pennypacker, J. S. (2006). Project management maturity: An assessment of project management capabilities among and between selected industries. IEEE Transactions on Engineering Management, 53, 59–68.
Hyvari, I.(2006). Project management effectiveness in project-oriented business organizations. Int J Project Manage, 24(3):216–25.

Jain, R. (1976). Decision making in the presence of fuzzy variables. IEEE Transactions on Systems Man and Cybernetics, 6, 698–703.
Jain, R. (1977). A procedure for multiple-aspect decision making using fuzzy sets. International Journal of Systems Science, 8, 1–7.
Kaufmann, Gupta, M.M. (1991). Introduction to Fuzzy Arithmetic: Theory and Application. Van Nostrand Reinhold, New York.
Kerzner, H., 2004. Advanced Project Management: Best Practices on Implementation. New jersey: John Wiley & Sons, Inc.
Kelly, J. E., Jr., & Walker,M. (1959). Critical path programming and scheduling. Proceedings of the Eastern Joint Computing Conference (pp. 160–173).
Kim H, Yukl G. Relationship of managerial effectiveness and advancement to self-reported and subordinate-reported leadership.
Liou, T.-S., & Wang, M.-J. (1992). Ranking fuzzy numbers with integral value. Fuzzy Sets and Systems, 50, 247–255.
Liu, X.W. & Han, S.L. (2005). Ranking fuzzy numbers with preference weighting function expectation. Computers and Mathematics with Applications 49, 1731-1753.
Marshall W. (1991). Leaders into the ’90s. Pers J, 70(5):80–6.
Morris PWG. (1988) Managing project interfaces. In: Cleland DI, King WR, (Eds.),Project management handbook. New York: Van Nostrand Reinhold.
Neely, A., Gregory, M., and Platts, K. (2005). Performance measurement system design: a literature review and research agenda. Int. J. Oper. Prod. Manage, 25 (12), 1228–1263.
Nidiffer, K., & Dolan, D. (2005). Evolving distributed project management. IEEE Software, 22(5), 63–72.
Pinder, J. A., & Marucheck, A. S. (1996). Using discounted cash flow heuristics to improve project net present value. Journal of Operations Management, 14, 229–240.
Pinto JK., & Slevin DP. (1988). Critical success factors in effective project implementation. In: Cleland DI, King WR, (Eds). Project management handbook. New York: Van Nostrand Reinhold.
Project Management Institute (2004). A Guide to the Project Management Body of Knowledge, PA.
Tsao, Chung T. (2009). Applying a fuzzy multiple criteria decision-making approach to the M&A due diligence. Expert Systems with Applications 36, 1559–1568.
Turner , J.R., & Müller, R. (2009). Leadership competency profiles of successfulproject managers. International Journal of Project Management.
Wang, Y. J., & Lee, S. H. (2008). The revised method of ranking fuzzy numbers with an area between the centroid and original points. Computers and Mathematics with Applications, 55, 2033–2042.
Williams, T. (2005). Assessing and moving on from the dominant project management discourse in the light of project overruns. IEEE Transactions on Engineering Management, 52(4), 497–508.
Zadeh, L.A. (1975). The concept of a linguistic variable and its application to approximate reasoning, part 1, 2 and 3. Information Science 8, 199-249, 301-357; 9 (1976) 43-80.
Zimmermann, H. J. (1991). Fuzzy Set Theory and its Applications, Fourth ed. Kluwer Academic Publisher. Boston.