Combining multiple criteria analysis, mathematical programming and Monte Carlo simulation to tackle uncertainty in Research and Development project portfolio selection: A case study from Greece

Research and Development (R&D) is a substantial activity in the modern economy of knowledge and therefore funding R&D activities is a challenging task for private and public institutions. The difficulty in evaluation of R&D projects is mainly the inherent uncertainty that has to be dealt with. In addition, when we have funding programs, various policy constraints for the allocation of funds must be also taken into account. In this paper we propose an R&D project portfolio selection method that deals with the inherent uncertainty of R&D project evaluation. Using decision rounds with multicriteria analysis, mathematical programming and Monte Carlo simulation in the framework of the Iterative Trichotomic Approach (ITA), we manage to tackle large problems. The method is applied in a case study from Greece with 2437 project proposals from the funding action Research-Create-Innovate with European and National resources. Groups of experts evaluate the projects in three criteria and complex policy constraints (geographical, sectoral etc.) are also applied. The iterative nature of ITA allows for gradually converging to the final portfolio of R&D projects. The results provide information not only about the acceptance or not of an R&D project to the final portfolio, but also deal with the main source of uncertainty which is the experts' evaluation, providing a degree of certainty for the selected and rejected projects. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The study proposes a method for selecting R&D project portfolios dealing with uncertainty in evaluation, using multicriteria analysis, mathematical programming, and Monte Carlo simulation. Through an iterative process, the final portfolio of R&D projects is gradually converged, addressing the uncertainty in expert evaluation.