Using mean-Gini and stochastic dominance to choose project portfolios with parameter uncertainty

Marcondes, Guilherme; Leme, Rafael Coradi; Leme, Marcela da Silveira; Silva, Carlos Eduardo Sanches da

doi:10.1080/0013791x.2016.1176283

Cited by 12 publications

(5 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another issue is that the largest the number of projects is, the higher the influence of uncertainty over the results will be. Thus fuzzy or stochastic approaches would be considered [9,26]. Figure 10 shows us the most common sizes of R&D project portfolios.…”

Section: 3%mentioning

confidence: 99%

“…Marcondes et al [26] Using mean-Gini and stochastic dominance to choose project portfolios with parameter uncertainty 2017…”

Section: Research Questions Findingsmentioning

confidence: 99%

“…Uncertainty generated by imprecise information [23][24][25]; 4. Uncertainty generated by limited data [26][27][28]; 5. Multiple interdependent and/or conflicting criteria (attributes and/or goals) [29]; 6.…”

Section: Introductionmentioning

confidence: 99%

“…It is worth mentioning that only a few articles give proper explanations of why they have chosen a specific MCDM method in their R&D PPS context [26,100]. In fact, to the best of our experience, there is no framework available in the literature that helps researchers select the best methods in each PPS case and other MCDM applications [50].…”

mentioning

confidence: 99%

“…How to consider resource constraints, interrelations, or mutual-exclusion to select projects? This approach appeared in Marcondes et al [26] that suggest a using Mean-Gini and stochastic dominance. A further question, how the approach responds to a real portfolio?…”

mentioning

confidence: 99%

See 4 more Smart Citations

MCDM-Based R&D Project Selection: A Systematic Literature Review

et al. 2021

View full text Add to dashboard Cite

From small spin-offs deploying innovative software to big pharmaceutical complexes making vaccines, Research and Development (R&D) Project Portfolio Selection (PPS) is an essential strategic process for various companies. It was never easy to select a set of projects among many feasible possibilities, even for yesterday’s paces. However, the world is rapidly changing, and so is R&D PPS. The portfolio objectives excel profit in the same manner that model constraints go beyond budget limitations. In parallel, project selection approaches and solving algorithms followed the increase of computational power. Despite all those changes, the importance of Multi-Criteria Decision Making (MCDM) methods and the decision criteria used for R&D PPS, there is still room for a systematic literature review (SLR) for the topic. Thus, this paper offers an SLR of the existing literature from the half-century, 1970, and onward MCDM-based R&D PPS performed in Scopus and Web of Science Core Collection. We provide a comprehensive picture of this field, show how it is changing, and highlight standard practices and research opportunities in the area. We perform a broad classification of the MCDM methods, categorized by the nature of alternatives, types of integration approach, the MCDM method itself, and types of uncertainty, by the 66 studies in the SLR database. The portfolios’ classification obeys the application domain and the number of projects. We have also explored all the 263 criteria found in the literature by grouping them according to experts from five Brazilian R&D organizations that together manage portfolios valued around US$ 5 billion a year, accounting for 38% of all Brazilian annual expenditure in R&D projects. We also include a bibliometric analysis of the considered papers and research opportunities highlighted or not explored by researchers. Given the increasing number of decision-making approaches and new technologies available, we hope to provide guidance on the topic and promote knowledge production and growth concerning the usage of MCDM methods and decision criteria in R&D PPS.

show abstract

Section: 3%mentioning

confidence: 99%

“…Marcondes et al [26] Using mean-Gini and stochastic dominance to choose project portfolios with parameter uncertainty 2017…”

Section: Research Questions Findingsmentioning

confidence: 99%