A Multi-criteria Assessment for R&D Innovation with Fuzzy Computing with Words

The assessment of research and development (R&D) innovation is inherently a multiple criteria decision making (MCDM) problem and has become a fundamental concern for R&D managers in the last decades. Research in identifying the relative importance of criteria used to select a favorable project has relied on subjective lists of criteria being presented to R&D managers. The conventional methods for evaluating corresponding R&D merits are inadequate for dealing with suchlike imprecise, heterogeneity or uncertainty of linguistic assessment. Whereas most attributes and their weights are linguistic variables and not easily quantifiable, 2-tuple fuzzy linguistic representation and multigranular linguistic computing manner are applied to transform the heterogeneous information assessed by multiple experts into a common domain and style. It is advantageous to retain consistency of evaluations. The proposed linguistic computing approach integrates the heterogeneity and determines the overall quality level and the performance with respect to specific quality attributes of an R&D innovation.

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

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

[3]  Avraham Shtub,et al.  R&D project evaluation: An integrated DEA and balanced scorecard approach ☆ , 2008 .

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

[5]  Francisco Herrera,et al.  A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[6]  James R. Freeland,et al.  Recent Advances in R&D Benefit Measurement and Project Selection Methods : Management Science , 1975 .

[7]  Wei-Shen Tai,et al.  A new evaluation model for intellectual capital based on computing with linguistic variable , 2009, Expert Syst. Appl..

[8]  A. Sivathanu Pillai,et al.  Performance measurement of R&D projects in a multi-project, concurrent engineering environment , 2002 .

[9]  Heung-Suk Hwang,et al.  R&D project evaluation model based on fuzzy set priority , 1998 .

[10]  Francisco Herrera,et al.  Incorporating filtering techniques in a fuzzy linguistic multi-agent model for information gathering on the web , 2004, Fuzzy Sets Syst..

[11]  Bowon Kim,et al.  An effective R&D performance measurement system: survey of Korean R&D researchers , 2002 .

[12]  R. P. Mohanty,et al.  A fuzzy ANP-based approach to R&D project selection: A case study , 2005 .

[13]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[14]  Hélène Sicotte,et al.  Integration mechanisms and R&D project performance☆ , 2000 .

[15]  Jindrich Klapka,et al.  Decision support system for multicriterial R&D and information systems projects selection , 2002, Eur. J. Oper. Res..

[16]  Wen-Pai Wang,et al.  Toward developing agility evaluation of mass customization systems using 2-tuple linguistic computing , 2009, Expert Syst. Appl..

[17]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[18]  Francisco Herrera,et al.  An Approach for Combining Linguistic and Numerical Information Based on the 2-Tuple Fuzzy Linguistic Representation Model in Decision-Making , 2000, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[19]  Francisco Herrera,et al.  Managing non-homogeneous information in group decision making , 2005, Eur. J. Oper. Res..

[20]  Mooyoung Jung,et al.  Modeling and analysis of project performance factors in an extended project-oriented virtual organization (EProVO) , 2010, Expert Syst. Appl..

[21]  Yu-An Huang,et al.  R&D sourcing strategies: Determinants and consequences , 2009 .

[22]  Pin-Yu Chu,et al.  A fuzzy AHP application in government-sponsored R&D project selection☆ , 2008 .