Adaptive stochastic manpower scheduling

Elmira Popova*, David Morton

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

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. Stochastic programs, unlike deterministic optimization models, 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 scheduling problem with uncertain uptimes of manufacturing equipment and uncertain production rates.

Original languageEnglish (US)
Pages (from-to)661-668
Number of pages8
JournalWinter Simulation Conference Proceedings
Volume1
StatePublished - Dec 1 1998

ASJC Scopus subject areas

  • Chemical Health and Safety
  • Software
  • Safety, Risk, Reliability and Quality
  • Applied Mathematics
  • Modeling and Simulation

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