Metamodel-Based Quantile Estimation for Hedging Control of Manufacturing Systems

Giulia Pedrielli, Russell R. Barton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Hedging-based control policies release a job into the system so that the probability of a job completing by its deadline is acceptable; job release decisions are based on quantile estimates of the job lead times. In multistage systems, these quantiles cannot be calculated analytically. In such cases, simulation can provide useful estimates, but computing a simulation-based quantile at the time of a job release decision is impractical. We explore a metamodeling approach based on efficient experiment design that can allow, after an offline learning phase, a metamodel estimate for the state-dependent lead time quantile. This allows for real time control if the metamodel is accurate, and computationally fast. In preliminary testing of a three-stage production system we find high accuracy for quadratic and cubic regression metamodels. These preliminary findings suggest that there is potential for metamodel-based hedging policies for real time control of manufacturing systems.

Original languageEnglish (US)
Title of host publication2019 Winter Simulation Conference, WSC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781728132839
StatePublished - Dec 2019
Event2019 Winter Simulation Conference, WSC 2019 - National Harbor, United States
Duration: Dec 8 2019Dec 11 2019

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Conference2019 Winter Simulation Conference, WSC 2019
Country/TerritoryUnited States
CityNational Harbor

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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