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Evaluation, ranking and selection of R&D projects by multiple experts: an evidential reasoning rule based approach

Abstract

As a typical multi-criteria group decision making (MCGDM) problem, research and development (R&D) project selection involves multiple decision criteria which are formulated by different frames of discernment, and multiple experts who are associated with different weights and reliabilities. The evidential reasoning (ER) rule is a rational and rigorous approach to deal with such MCGDM problems and can generate comprehensive distributed evaluation outcomes for each R&D project. In this paper, an ER rule based model taking into consideration experts’ weights and reliabilities is proposed for R&D project selection. In the proposed approach, a utility based information transformation technique is applied to handle qualitative evaluation criteria with different evaluation grades, and both adaptive weights of criteria and utilities assigned to evaluation grades are introduced to the ER rule based model. A nonlinear optimisation model is developed for the training of weights and utilities. A case study with the National Science Foundation of China is conducted to demonstrate how the proposed method can be used to support R&D project selection. Validation data show that the evaluation results become more reliable and consistent with reality by using the trained weights and utilities from historical data.

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Acknowledgements

This research is partially supported by the National Natural Science Foundation of China under Grant No. 71071048 and 71601060 and the Scholarship from China Scholarship Council under Grant No. 201306230047.

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Correspondence to Fang Liu.

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Liu, F., Zhu, Wd., Chen, Yw. et al. Evaluation, ranking and selection of R&D projects by multiple experts: an evidential reasoning rule based approach. Scientometrics 111, 1501–1519 (2017). https://doi.org/10.1007/s11192-017-2278-1

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  • DOI: https://doi.org/10.1007/s11192-017-2278-1

Keywords

  • R&D project evaluation
  • Evidential reasoning
  • Reliability
  • Nonlinear optimisation