Fuzzy MCDM approach to R&D project evaluation in Taiwan’s public sectors

In most industrialized countries, the role of public sectors has been undergoing massive changes in the past several decades. One of the major reformations in public sectors is the pursuit of the ability to distinguish between executive performances. Thus establishing a performance-oriented evaluation in public sectors is the key to successful administrations. However, because of lacking relative comparable measuring standards, it is difficult to measure the relative performance of one unit while comparing to other units with regard to the multiple criteria decision making (MCDM) of performance evaluation. This study mainly focuses on the performance ranking of research and development (R&D) projects in Taiwanpsilas public sectors. The algorithm in this study is based on the concept of fuzzy set theory and the hierarchical structure analysis. The analyzing method adopts the methods of standard normal distribution, linear transformation and fuzzy MCDM, carrying on the analysis of multiple criteria of the performance evaluation. In order to differentiate the indistinct linguistic terms and make the performance evaluation be able to match true conditions, the characteristic of adopting fuzzy set theory in this study is to construct the membership function for those criteria of performance evaluation. In the meantime, this study constructs linguistic values to the subjective judgments and analyzes the ranking results of the performance evaluation with respect to 45 R&D projects of one of Taiwanpsilas electric power companies.

[1]  H. J. Harrington,et al.  The Improvement Process: How America's Leading Companies Improve Quality , 1987 .

[2]  Mark Graham Brown,et al.  Keeping Score: Using the Right Metrics to Drive World-Class Performance , 1996 .

[3]  Majid Nojavan,et al.  A fuzzy ranking method by desirability index , 2006, J. Intell. Fuzzy Syst..

[4]  John H. Jackson,et al.  Personnel: Human resource management , 1985 .

[5]  G. Bortolan,et al.  A review of some methods for ranking fuzzy subsets , 1985 .

[6]  Kuo-Hui Tsai,et al.  Identification of fuzzy system based on gray relation , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[7]  C. Hwang,et al.  Group Decision Making Under Multiple Criteria: Methods and Applications , 1986 .

[8]  Raynold A. Svenson,et al.  Measuring R&D Productivity , 1988 .

[9]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[10]  Christer Carlsson,et al.  Fuzzy multiple criteria decision making: Recent developments , 1996, Fuzzy Sets Syst..

[11]  Pao-Long Chang,et al.  A fuzzy multi-criteria decision making method for technology transfer strategy selection in biotechnology , 1994 .

[12]  A. Neely The performance measurement revolution: why now and what next? , 1999 .

[13]  H. G. Heneman Personnel / Human Resource Management , 1989 .

[14]  R. Kaplan,et al.  Using the balanced scorecard as a strategic management system , 1996 .

[15]  Gin-Shuh Liang,et al.  Fuzzy MCDM based on ideal and anti-ideal concepts , 1999, Eur. J. Oper. Res..

[16]  A. D. Szilagyi,et al.  Management and Performance , 2013 .

[17]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[18]  Andy Neely,et al.  Designing, implementing and updating performance measurement systems , 2000 .

[19]  H. Hatry The Status of Productivity Measurement in the Public Sector , 1978 .

[20]  Andrew S. Chang,et al.  Development of Consultant Performance Measures for Design Projects , 1998 .

[21]  Shan-Huo Chen Ranking fuzzy numbers with maximizing set and minimizing set , 1985 .

[22]  K. Kim,et al.  Ranking fuzzy numbers with index of optimism , 1990 .

[23]  Gin-Shuh Liang,et al.  Computing, Artificial Intelligence and Information Technology Cluster analysis based on fuzzy equivalence relation , 2005 .

[24]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[25]  L. M. D. C. Ibáñez,et al.  A subjective approach for ranking fuzzy numbers , 1989 .

[26]  Sang M. Lee,et al.  An expert system for multiobjective decision making: application of fuzzy linguistic preferences and goal programming , 2002, Fuzzy Sets Syst..

[27]  Mitchell M. Tseng,et al.  Fuzzy Ranking for Concept Evaluation in Configuration Design for Mass Customization , 1998 .

[28]  E. Geisler,et al.  An integrated cost-performance model of research and development evaluation , 1995 .

[29]  Warren B. Brown,et al.  Observations on the measurement of R&D productivity: a case study , 1992 .

[30]  Sukhamay Kundu Replacing trapezoidal membership functions by triangular membership functions for /spl otimes/-transitivity , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[31]  L. Dyer,et al.  Human resource strategies and firm performance: what do we know and where do we need to go? , 1995 .

[32]  H. John Bernardin,et al.  Performance appraisal : assessing human behavior at work , 1984 .

[33]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[34]  Chinho Lin,et al.  A new fuzzy TOPSIS for fuzzy MADM problems under group decisions , 2007, J. Intell. Fuzzy Syst..

[35]  Da Ruan,et al.  Fuzzy group decision making for selection among computer integrated manufacturing systems , 2003, Comput. Ind..