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R&D Project Selection and Manpower Allocation with Integer Nonlinear Goal Programming

Author

Listed:
  • Bernard W. Taylor, III

    (Virginia Polytechnic Institute and State University)

  • Laurence J. Moore

    (Virginia Polytechnic Institute and State University)

  • Edward R. Clayton

    (Virginia Polytechnic Institute and State University)

Abstract

A number of recent research efforts in the area of research and development planning have indicated the necessity that the R&D project selection process be viewed as a multi-criteria decision-making problem. As a result, linear 0-1 goal programming, because of its ability to encompass multiple objectives, has been employed on several occasions as a project selection model. However, in these goal programming models the relationships between resource utilization and project outcomes or between various resource utilizations have been expressed linearly when, in reality, they are often non-linear. For example, as the resources allocated to a project are increased the probability of project success will also increase but at a decreasing rate. In this paper, a non-linear integer goal programming model is described via a case example. The case example encompasses a pool of thirty researchers available for allocation to seven possible R&D projects. As such, the model consists of integer decision variables for both the number of researchers allocated, and, project selection. Researcher allocation and project selection are subject to several linear and nonlinear goal constraints. Nonlinear goal constraints are constructed that relate the probability of project success to the number of researchers assigned to a project and to expected monetary return, and, that relate the number of researchers allocated to project completion time. Linear goal constraints are developed for budget limitations, computer capacity utilization and various strict conditions placed on the model. The model selects projects and allocates researchers to projects such that a prioritized goal structure is most satisfactorily achieved. The model solution of the case example indicated the selection of five of the seven projects and the number of researchers assigned to each project. Of the nine prioritized goals, six were achieved while three were only partially achieved.

Suggested Citation

  • Bernard W. Taylor, III & Laurence J. Moore & Edward R. Clayton, 1982. "R&D Project Selection and Manpower Allocation with Integer Nonlinear Goal Programming," Management Science, INFORMS, vol. 28(10), pages 1149-1158, October.
    • Handle: RePEc:inm:ormnsc:v:28:y:1982:i:10:p:1149-1158
    DOI: 10.1287/mnsc.28.10.1149
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    Citations

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    Cited by:

    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. Cinzia Colapinto & Raja Jayaraman & Simone Marsiglio, 2017. "Multi-criteria decision analysis with goal programming in engineering, management and social sciences: a state-of-the art review," Annals of Operations Research, Springer, vol. 251(1), pages 7-40, April.
    3. Farrell E. Jensen, 1985. "Allocating synthesis R & D resources in the agricultural chemicals industry," Agribusiness, John Wiley & Sons, Ltd., vol. 1(3), pages 227-235.
    4. Bertolini, Massimo & Bevilacqua, Maurizio, 2006. "A combined goal programming—AHP approach to maintenance selection problem," Reliability Engineering and System Safety, Elsevier, vol. 91(7), pages 839-848.
    5. Pal, Bijay Baran & Nath Moitra, Bhola, 2003. "A goal programming procedure for solving problems with multiple fuzzy goals using dynamic programming," European Journal of Operational Research, Elsevier, vol. 144(3), pages 480-491, February.
    6. Madjid Tavana, 2003. "CROSS: A Multicriteria Group-Decision-Making Model for Evaluating and Prioritizing Advanced-Technology Projects at NASA," Interfaces, INFORMS, vol. 33(3), pages 40-56, June.
    7. Chen, Jiaqiong & Askin, Ronald G., 2009. "Project selection, scheduling and resource allocation with time dependent returns," European Journal of Operational Research, Elsevier, vol. 193(1), pages 23-34, February.
    8. Hassanzadeh, Farhad & Modarres, Mohammad & Nemati, Hamid R. & Amoako-Gyampah, Kwasi, 2014. "A robust R&D project portfolio optimization model for pharmaceutical contract research organizations," International Journal of Production Economics, Elsevier, vol. 158(C), pages 18-27.
    9. Yuichi Takano & Nobuaki Ishii & Masaaki Muraki, 2017. "Multi-period resource allocation for estimating project costs in competitive bidding," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(2), pages 303-323, June.
    10. Ye Tian & Miao Sun & Zuoliang Ye & Wei Yang, 2016. "Expanded models of the project portfolio selection problem with loss in divisibility," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(8), pages 1097-1107, August.
    11. Supachart Iamratanakul, 2013. "The Selection of Project in Rapid Environment’s Industry Using Zero Based Budget," Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management,, ToKnowPress.
    12. A Morton & D Bird & A Jones & M White, 2011. "Decision conferencing for science prioritisation in the UK public sector: a dual case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 50-59, January.

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