A fuzzy AHP application in government-sponsored R&D project selection☆

Due to the funding scale and complexity of technology, the selection of government sponsored technology development projects can be viewed as a multiple-attribute decision that is normally made by a review committee with experts from academia, industry, and the government. In this paper, we present a fuzzy analytic hierarchy process method and utilize crisp judgment matrix to evaluate subjective expert judgments made by the technical committee of the Industrial Technology Development Program in Taiwan. Our results indicate that the scientific and technological merit is the most important evaluation criterion considered in overall technical committees. We demonstrate how the relative importance of the evaluation criteria changes under various risk environments via simulation.

[1]  C. K. Kwong,et al.  A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment , 2002, J. Intell. Manuf..

[2]  R. D. Badinelli,et al.  A concurrent optimization methodology for concurrent engineering , 1999 .

[3]  J. Buckley,et al.  Fuzzy hierarchical analysis , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[4]  L. C. Rabelo,et al.  An integrated artificial neural networks model for industrial projects risk assessment , 1992, [Proceedings] 1992 International Engineering Management Conference.

[5]  D. Kutlaca.  Multicriteria-based procedure as decision support in the selection of government financed R&D projects , 1997 .

[6]  Maryann P. Feldman,et al.  Leveraging Research and Development: Assessing the Impact of the U.S. Advanced Technology Program , 2003 .

[7]  Richard Bellman,et al.  Decision-making in fuzzy environment , 2012 .

[8]  Thomas L. Saaty,et al.  The Analytic Network Process: Decision Making With Dependence and Feedback , 2001 .

[9]  T. Saaty.  Analytic Hierarchy Process , 2005 .

[10]  Thomas Åstebro,et al.  Key success factors for technological entrepreneurs' R&D projects , 2004, IEEE Transactions on Engineering Management.

[11]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[12]  W. Pedrycz.  Why triangular membership functions , 1994 .

[13]  Bernard W. Taylor,et al.  Multiple criteria R&D project selection and scheduling using fuzzy logic , 1996, Comput. Oper. Res..

[14]  Hidetomo Ichihashi,et al.  Analytic hierarchy process based on interval analysis , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[15]  Ludmil Mikhailov,et al.  Deriving priorities from fuzzy pairwise comparison judgements , 2003, Fuzzy Sets Syst..

[16]  A. D. Henriksen,et al.  A practical R&D project-selection scoring tool , 1999 .

[17]  J. Z. Shyu,et al.  Fuzzy Multiple Criteria Selection of Government-Sponsored Frontier Technology R&D Projects , 2003 .

[18]  Neil R. Baker,et al.  R&D Project Selection Models: An Assessment , 1974, R&D Management.

[19]  Adrien Presley,et al.  R&D project selection using the analytic network process , 2002, IEEE Trans. Engineering Management.

[20]  Baruch Raz,et al.  Technological Aspects of Project Selection , 1982 .

[21]  Chen-Fu Chien,et al.  A Portfolio-Evaluation Framework for Selecting R&D Projects , 2002 .

[22]  Rita Almeida Ribeiro.  Fuzzy multiple attribute decision making: A review and new preference elicitation techniques , 1996, Fuzzy Sets Syst..

[23]  Ching-Hsue Cheng.  Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function , 1997 .

[24]  Craig Lawson,et al.  A decision framework for R&D project selection , 2002, IEEE International Engineering Management Conference.

[25]  J. Buckley.  The multiple judge, multiple criteria ranking problem: A fuzzy set approach , 1984 .

[26]  Zhang Huiying,et al.  Research for application of group decision method on appraisement system of science research schema , 2002, Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527).

[27]  Radhika Santhanam,et al.  A multiple criteria decision model for information system project selection , 1995, Comput. Oper. Res..

[28]  James R. Freeland,et al.  Recent progress in modeling R&D project-selection processes , 1992 .

[29]  Muhittin Oral,et al.  A methodology for collective evaluation and selection of industrial R&D projects , 1991 .

[30]  Thomas L. Saaty,et al.  Decision making with dependence and feedback : the analytic network process : the organization and prioritization of complexity , 1996 .

[31]  Kiyong Om,et al.  Different factors considered in project selection at public and private R&D institutes , 1996 .

[32]  Ahmad A. Ghosn,et al.  A management model for technology and R&D selection , 1997 .

[33]  Don-lin Mon,et al.  Evaluating weapon system by Analytical Hierarchy Process based on fuzzy scales , 1994 .

[34]  Ching-Hsue Cheng,et al.  Evaluating weapon systems using ranking fuzzy numbers , 1999, Fuzzy Sets Syst..

[35]  Felix T.S. Chan,et al.  Evaluation methodologies for technology selection , 2000 .

[36]  Matthew J. Liberatore,et al.  The Practice of Management Science in R&D Project Management : Management Science , 1983 .

[37]  M. A. Mustafa,et al.  Project risk assessment using the analytic hierarchy process , 1991 .

[38]  James J. Buckley,et al.  Fuzzy hierarchical analysis: the Lambda-Max method , 2001, Fuzzy Sets Syst..

[39]  G. Tonkay,et al.  Application of modified fuzzy ahp method to analyze bolting sequence of structural joints , 1995 .

[40]  Matthew J. Liberatore,et al.  An extension of the analytic hierarchy process for industrial R&D project selection and resource allocation , 1987, IEEE Transactions on Engineering Management.

[41]  Nicholas Bilalis,et al.  A fuzzy sets approach to new product portfolio management , 2002, IEEE International Engineering Management Conference.

[42]  Shyi-Ming Chen,et al.  Fuzzy group decision making for evaluating the rate of aggregative risk in software development , 2001, Fuzzy Sets Syst..

[43]  W. Pedrycz,et al.  A fuzzy extension of Saaty's priority theory , 1983 .

[44]  Giovanni Perrone.  Fuzzy multiple criteria decision model for the evaluation of AMS , 1994 .

[45]  Chen-Tung Chen,et al.  Extensions of the TOPSIS for group decision-making under fuzzy environment , 2000, Fuzzy Sets Syst..

[46]  R. Stewart,et al.  IT/IS projects selection using multi‐criteria utility theory , 2002 .

[47]  John H. Friar,et al.  Factors for success in R&D projects and new product innovation: a contextual framework , 1997 .

[48]  Chun Hu,et al.  Analytic hierarchy process with fuzzy scoring in evaluating multidisciplinary R&D projects in China , 2005, IEEE Transactions on Engineering Management.