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Parallel Funding of R&D Tasks with Probabilistic Outcomes

Author

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  • Jonathan F. Bard

    (Department of Mechanical Engineering, University of Texas, Austin, Texas 78712)

Abstract

This paper addresses the problem commonly faced by R&D managers of funding redundant R&D tasks across several stages or components of a project. In the proposed methodology it is assumed that task outcomes are random, but that their distribution can be determined from engineering inputs. In order to account for individual preferences a utility function is used to measure overall project results, and hence guide the decisions at each stage. The problem is formulated as a probabilistic network and solved by means of a heuristic comprising simulation and dynamic programming. Although computational experience is limited, the results obtained from a number of examples associated with the development of a photovoltaic solar module were quite promising; that is, the heuristic always found the optimal investment plan. In general, computational times were minimal when compared to data collection efforts, with the latter perhaps being the principal hurdle to full implementation. The sponsoring organization, however, felt that the unified approach offered by the methodology represented a major improvement over current analytic techniques.

Suggested Citation

  • Jonathan F. Bard, 1985. "Parallel Funding of R&D Tasks with Probabilistic Outcomes," Management Science, INFORMS, vol. 31(7), pages 814-828, July.
  • Handle: RePEc:inm:ormnsc:v:31:y:1985:i:7:p:814-828
    DOI: 10.1287/mnsc.31.7.814
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    Citations

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    Cited by:

    1. Boaz Golany & Moshe Kress & Michal Penn & Uriel G. Rothblum, 2012. "Resource allocation in an asymmetric technology race with temporary advantages," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(2), pages 128-145, March.
    2. Scott A. Shane & Karl T. Ulrich, 2004. "50th Anniversary Article: Technological Innovation, Product Development, and Entrepreneurship in Management Science," Management Science, INFORMS, vol. 50(2), pages 133-144, February.
    3. Yigal Gerchak & Christian Schmid, 2022. "Principal–agent models where a principal is only affected by extreme performances," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(2), pages 468-477, March.
    4. B. De Reyck & Y. Grushka-Cockayne & R. Leus, 2007. "A New Challenge in Project Scheduling. The Incorporation of Activity Failures," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(3), pages 410-435.
    5. Wenhui Zhao & Nicholas G. Hall & Zhixin Liu, 2020. "Project Evaluation and Selection with Task Failures," Production and Operations Management, Production and Operations Management Society, vol. 29(2), pages 428-446, February.
    6. Murthy, Ishwar & Sarkar, Sumit, 1997. "Exact algorithms for the stochastic shortest path problem with a decreasing deadline utility function," European Journal of Operational Research, Elsevier, vol. 103(1), pages 209-229, November.

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