A Bayesian stochastic programming approach to an employee scheduling problem

David P. Morton*, Elmira Popova

*Corresponding author for this work

Research output: Contribution to journalArticle

21 Scopus citations


Bayesian forecasting models provide distributional estimates for random parameters, and relative to classical schemes, have the advantage that they can rapidly capture changes in nonstationary systems using limited historical data. Unlike deterministic optimization, stochastic programs explicitly incorporate distributions for random parameters in the model formulation, and thus have the advantage that the resulting solutions more fully hedge against future contingencies. In this paper, we exploit the strengths of Bayesian prediction and stochastic programming in a rolling-horizon approach that can be applied to solve real-world problems. We illustrate the methodology on an employee production scheduling problem with uncertain up-times of manufacturing equipment and uncertain production rates. Computational results indicate the value of our approach.

Original languageEnglish (US)
Pages (from-to)155-167
Number of pages13
JournalIIE Transactions (Institute of Industrial Engineers)
Issue number2
StatePublished - Feb 1 2004

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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