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Theoretical Paper

Two-resource stochastic capacity planning employing a Bayesian methodology

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Pages 1198-1208 | Received 01 Aug 2001, Accepted 01 Apr 2003, Published online: 21 Dec 2017
 

Abstract

We examine a stochastic capacity-planning problem with two resources that can satisfy demand for two services. One of the resources can only satisfy demand for a specific service, whereas the other resource can provide both services. We formulate the problem of choosing the capacity levels of each resource to maximize expected profits. In addition, we provide analytic, easy-to-interpret optimal solutions, as well as perform a comparative statics analysis. As applying the optimal solutions effectively requires good estimates of the unknown demand parameters, we also examine Bayesian estimates of the demand parameters derived via a class of conjugate priors. We compare the optimal expected profits when demands for the two services follow independent distributions with informative and non-informative priors, and demonstrate that using good informative priors on demand can significantly improve performance.

Acknowledgements

The authors would like to thank the editor and two anonymous referees for their helpful comments.

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