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Balancing and optimizing a portfolio of R&D projects

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  • George J. Beaujon
  • Samuel P. Marin
  • Gary C. McDonald

Abstract

A mathematical formulation of an optimization model designed to select projects for inclusion in an R&D portfolio, subject to a wide variety of constraints (e.g., capital, headcount, strategic intent, etc.), is presented. The model is similar to others that have previously appeared in the literature and is in the form of a mixed integer programming (MIP) problem known as the multidimensional knapsack problem. Exact solution of such problems is generally difficult, but can be accomplished in reasonable time using specialized algorithms. The main contribution of this paper is an examination of two important issues related to formulation of project selection models such as the one presented here. If partial funding and implementation of projects is allowed, the resulting formulation is a linear programming (LP) problem which can be solved quite easily. Several plausible assumptions about how partial funding impacts project value are presented. In general, our examples suggest that the problem might best be formulated as a nonlinear programming (NLP) problem, but that there is a need for further research to determine an appropriate expression for the value of a partially funded project. In light of that gap in the current body of knowledge and for practical reasons, the LP relaxation of this model is preferred. The LP relaxation can be implemented in a spreadsheet (even for relatively large problems) and gives reasonable results when applied to a test problem based on GM's R&D project selection process. There has been much discussion in the literature on the topic of assigning a quantitative measure of value to each project. Although many alternatives are suggested, no one way is universally accepted as the preferred way. There does seem to be general agreement that all of the proposed methods are subject to considerable uncertainty. A systematic way to examine the sensitivity of project selection decisions to variations in the measure of value is developed. It is shown that the solution for the illustrative problem is reasonably robust to rather large variations in the measure of value. We cannot, however, conclude that this would be the case in general. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 18–40, 2001

Suggested Citation

  • George J. Beaujon & Samuel P. Marin & Gary C. McDonald, 2001. "Balancing and optimizing a portfolio of R&D projects," Naval Research Logistics (NRL), John Wiley & Sons, vol. 48(1), pages 18-40, February.
    • Handle: RePEc:wly:navres:v:48:y:2001:i:1:p:18-40
    DOI: 10.1002/1520-6750(200102)48:13.0.CO;2-7
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    References listed on IDEAS

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    1. Kyparisis, George J. & Gupta, Sushil K. & Ip, Chi-Ming, 1996. "Project selection with discounted returns and multiple constraints," European Journal of Operational Research, Elsevier, vol. 94(1), pages 87-96, October.
    2. Avinash K. Dixit & Robert S. Pindyck, 1994. "Investment under Uncertainty," Economics Books, Princeton University Press, edition 1, number 5474.
    3. Bruce Hoadley & Paul Katz & Amir Sadrian, 1993. "Improving the Utility of the Bellcore Consortium," Interfaces, INFORMS, vol. 23(1), pages 27-43, February.
    4. William M. Burnett & Barry G. Silverman & Dominic J. Monetta, 1993. "R&D Project Appraisal at the Gas Research Institute: Part II," Operations Research, INFORMS, vol. 41(6), pages 1020-1032, December.
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    1. Lai, Xiangjing & Hao, Jin-Kao & Yue, Dong, 2019. "Two-stage solution-based tabu search for the multidemand multidimensional knapsack problem," European Journal of Operational Research, Elsevier, vol. 274(1), pages 35-48.
    2. Çağlar, Musa & Gürel, Sinan, 2019. "Impact assessment based sectoral balancing in public R&D project portfolio selection," Socio-Economic Planning Sciences, Elsevier, vol. 66(C), pages 68-81.
    3. José García & Paola Moraga & Broderick Crawford & Ricardo Soto & Hernan Pinto, 2022. "Binarization Technique Comparisons of Swarm Intelligence Algorithm: An Application to the Multi-Demand Multidimensional Knapsack Problem," Mathematics, MDPI, vol. 10(17), pages 1-20, September.
    4. Al-Shihabi, Sameh, 2021. "A Novel Core-Based Optimization Framework for Binary Integer Programs- the Multidemand Multidimesional Knapsack Problem as a Test Problem," Operations Research Perspectives, Elsevier, vol. 8(C).
    5. Hongbo Li & Rui Chen & Xianchao Zhang, 2022. "Uncertain Public R&D Project Portfolio Selection Considering Sectoral Balancing and Project Failure," Sustainability, MDPI, vol. 14(23), pages 1-13, November.
    6. Solak, Senay & Li, Zhuoxin & Gumus, Mehmet & Zhuo, Yueran, 2024. "A budgeting resource allocation model for capacity expansion," International Journal of Production Economics, Elsevier, vol. 273(C).
    7. Fukasawa, Ricardo & Naoum-Sawaya, Joe & Oliveira, Daniel, 2024. "The price-elastic knapsack problem," Omega, Elsevier, vol. 124(C).
    8. Arnaud Fréville & SaÏd Hanafi, 2005. "The Multidimensional 0-1 Knapsack Problem—Bounds and Computational Aspects," Annals of Operations Research, Springer, vol. 139(1), pages 195-227, October.
    9. Dalton Garcia Borges de Souza & Erivelton Antonio dos Santos & Nei Yoshihiro Soma & Carlos Eduardo Sanches da Silva, 2021. "MCDM-Based R&D Project Selection: A Systematic Literature Review," Sustainability, MDPI, vol. 13(21), pages 1-34, October.

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