Two-resource stochastic capacity planning employing a Bayesian methodology

E. Stavrulaki, D. K.H. Fong, D. K.J. Lin

Research output: Contribution to journalArticle

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1198-1208
Number of pages11
JournalJournal of the Operational Research Society
Volume54
Issue number11
DOIs
StatePublished - Nov 2003

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Strategy and Management
  • Management Science and Operations Research
  • Marketing

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