IDEAS home Printed from https://ideas.repec.org/a/wly/navres/v48y2001i1p18-40.html
   My bibliography  Save this article

Balancing and optimizing a portfolio of R&D projects

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/1520-6750(200102)48:13.0.CO;2-7
    Download Restriction: no

    File URL: https://libkey.io/10.1002/1520-6750(200102)48:13.0.CO;2-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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. Fukasawa, Ricardo & Naoum-Sawaya, Joe & Oliveira, Daniel, 2024. "The price-elastic knapsack problem," Omega, Elsevier, vol. 124(C).
    7. 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.
    8. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Oscar Gutiérrez & Francisco Ruiz-Aliseda, 2011. "Real options with unknown-date events," Annals of Finance, Springer, vol. 7(2), pages 171-198, May.
    2. Arve, Malin & Zwart, Gijsbert, 2023. "Optimal procurement and investment in new technologies under uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 147(C).
    3. Marks, Phillipa & Marks, Brian, 2007. "Spectrum Allocation, Spectrum Commons and Public Goods: the Role of the Market," MPRA Paper 6785, University Library of Munich, Germany.
    4. Pierre‐Richard Agénor, 2004. "Macroeconomic Adjustment and the Poor: Analytical Issues and Cross‐Country Evidence," Journal of Economic Surveys, Wiley Blackwell, vol. 18(3), pages 351-408, July.
    5. Atal, Vidya & Bar, Talia & Gordon, Sidartha, 2016. "Project selection: Commitment and competition," Games and Economic Behavior, Elsevier, vol. 96(C), pages 30-48.
    6. Prelipcean, Gabriela & Boscoianu, Mircea, 2019. "Aspect Regarding the Design of Active Strategies for Venture Capital Financing – the Flexible Adjustment for Romania as a Frontier Capital Market," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2019), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019, pages 187-196, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    7. Waters, James, 2015. "Optimal design and consequences of financial disclosure regulation: a real options approach," MPRA Paper 63369, University Library of Munich, Germany.
    8. Golub, Alexander (Голуб, Александр), 2018. "Methodological Issues of Assessing Investment Risks in Projects Weakening the Dependence of the Russian Economy on Natural Resources and Providing a Transition to Low-Carbon Development [Методологи," Working Papers 071802, Russian Presidential Academy of National Economy and Public Administration.
    9. Suleyman Basak & Georgy Chabakauri, 2012. "Dynamic Hedging in Incomplete Markets: A Simple Solution," The Review of Financial Studies, Society for Financial Studies, vol. 25(6), pages 1845-1896.
    10. Casper Agaton, 2017. "Coal, Renewable, or Nuclear? A Real Options Approach to Energy Investments in the Philippines," International Journal of Sustainable Energy and Environmental Research, Conscientia Beam, vol. 6(2), pages 50-62.
    11. Pringles, Rolando & Olsina, Fernando & Penizzotto, Franco, 2020. "Valuation of defer and relocation options in photovoltaic generation investments by a stochastic simulation-based method," Renewable Energy, Elsevier, vol. 151(C), pages 846-864.
    12. Jaewon Jung, 2023. "Multinational Firms and Economic Integration: The Role of Global Uncertainty," Sustainability, MDPI, vol. 15(3), pages 1-18, February.
    13. Alvarez, Luis H. R., 1998. "Exit strategies and price uncertainty: a Greenian approach," Journal of Mathematical Economics, Elsevier, vol. 29(1), pages 43-56, January.
    14. Arora, Ashish, 1999. "Exploring the internalization rationale for international investment: wholly owned subsidiary versus technology licensing in the worldwide chemical industry," DEE - Working Papers. Business Economics. WB 6430, Universidad Carlos III de Madrid. Departamento de Economía de la Empresa.
    15. Shunsuke Managi & Zheng Zhang & Shinya Horie, 2016. "A real options approach to environmental R&D project evaluation," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 18(3), pages 359-394, July.
    16. Chen Yu-Fu & Funke Michael, 2004. "Working Time and Employment Under Uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(3), pages 1-23, September.
    17. Nick Bloom & Stephen Bond & John Van Reenen, 2007. "Uncertainty and Investment Dynamics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 74(2), pages 391-415.
    18. Keppler, Jan Horst & Quemin, Simon & Saguan, Marcelo, 2022. "Why the sustainable provision of low-carbon electricity needs hybrid markets," Energy Policy, Elsevier, vol. 171(C).
    19. Song, Dandan & Wang, Huamao & Yang, Zhaojun, 2014. "Learning, pricing, timing and hedging of the option to invest for perpetual cash flows with idiosyncratic risk," Journal of Mathematical Economics, Elsevier, vol. 51(C), pages 1-11.
    20. Khanh Hoang, 2022. "How does corporate R&D investment respond to climate policy uncertainty? Evidence from heavy emitter firms in the United States," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 29(4), pages 936-949, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:navres:v:48:y:2001:i:1:p:18-40. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1520-6750 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.