Hostname: page-component-848d4c4894-hfldf Total loading time: 0 Render date: 2024-05-15T00:38:07.376Z Has data issue: false hasContentIssue false
  • English
  • Français

Resource allocation in speculative chemical research

Published online by Cambridge University Press:  14 July 2016

J. C. Gittins
J. C. Gittins*
Affiliation:
University Engineering Department, Cambridge
Get access Rights & Permissions [Opens in a new window]

Abstract

A stochastic theory is given for speculative industrial chemical research. It is shown how the theory may be used to suggest a suitable balance between long-term and short-term projects.

Keywords

RESOURCE ALLOCATIONCHEMICAL RESEARCH
Type
Research Papers
Information
Journal of Applied Probability , Volume 11 , Issue 2 , June 1974 , pp. 255 - 265
Copyright
Copyright © Applied Probability Trust 1974 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Cetron, M. J., Martino, J. and Roepeke, L. (1967) The selection of R & D program content. Survey of quantitative methods. IEEE Trans. Engineering Management EM–14, 4–13.Google Scholar
Gittins, J. C. (1967) A generalization of a result in linear programming. J. Inst. Math. Appl. 3, 193–201.CrossRefGoogle Scholar
Gittins, J. C. (1969) Optimal resource allocation in chemical research. Adv. Appl. Prob. 1, 238–270.Google Scholar
Gittins, J. C. (1972) Some problems of stochastic resource allocation. J. Appl. Prob. 9, 360–369.Google Scholar
Gittins, J. C. (1973) Speculative chemical research — how many eggs in a basket? R & D Management 3, 71–81.Google Scholar
Kamien, M. I. and Schwartz, N. L. (1971) Expenditure patterns for risky R and D projects. J. Appl. Prob. 8, 60–73.Google Scholar
Laska, E., Meisner, M. and Siegel, C. (1972) Contributions to the theory of optimal resource allocation. J. Appl. Prob. 9, 337–359.Google Scholar
Lucas, R. E. (1971) Optimal management of an R & D project. Management Sci. 17, A679–A697.Google Scholar
Moore, J. R. and Baker, N. R. (1969) Computational analysis of scoring models for R & D project selection. Management Sci. 16, B212–B232.Google Scholar
Saaty, T. S. (1959) Mathematical Methods of Operational Research. McGraw-Hill, London.Google Scholar