Two-resource stochastic capacity planning employing a Bayesian methodology

E Stavrulaki (), D K H Fong and D K J Lin
Additional contact information
E Stavrulaki: Bentley College
D K H Fong: Penn State University, University Park
D K J Lin: Penn State University, University Park

Journal of the Operational Research Society, 2003, vol. 54, issue 11, 1198-1208

Abstract: 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.

Keywords: applied probability; Bayesian methodology; capacity planning; parameter estimation; single-period inventory models (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (2)

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DOI: 10.1057/palgrave.jors.2601607

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