Abstract
Mathematical programming methods have been suggested and used as an aid to R & D project portfolio selection. One of the main criticisms of the use of such models is that the stochastic nature of the problem has been largely ignored. This paper presents a method which takes into account the stochastic nature of resource requirements and project benefits, using a combination of probabilistic networks, simulation and mathematical programming. A case study based on data from an industrial R & D laboratory is presented and compared with the use of expected value methods. The results of the study indicate that in this particular case the deterministic linear programming solution is robust.
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†Now with the Manchester Business School.
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Lockett, A., Freeman, P. Probabilistic Networks and R & D Portfolio Selection. J Oper Res Soc 21, 353–359 (1970). https://doi.org/10.1057/jors.1970.77
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DOI: https://doi.org/10.1057/jors.1970.77