Estimating Clearing Functions for Production Resources Using Simulation Optimization

We implement a gradient-based simulation optimization approach, the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, to estimate clearing functions (CFs) that describe the expected output of a production resource as a function of its expected workload from empirical data. Instead...

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Bibliographic Details
Published in:IEEE transactions on automation science and engineering Vol. 12; no. 2; pp. 539 - 552
Main Authors: Kacar, Necip Baris, Uzsoy, Reha
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Automation
Clearing function
Data models
linear programming
Optimization
Optimization algorithms
Planning
Production planning
Production systems
Simulation
simulation optimization
Stochastic models
workload-dependent lead times
ISSN:1545-5955, 1558-3783
Online Access:Get full text
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Summary:We implement a gradient-based simulation optimization approach, the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, to estimate clearing functions (CFs) that describe the expected output of a production resource as a function of its expected workload from empirical data. Instead of trying to optimize the fit of the CF to the data, we seek values of the CF parameters that optimize the expected performance for the system when the fitted CFs are used to develop release schedules. A simulation model of a scaled-down wafer fabrication facility is used to generate the data and evaluate the performance of the CFs obtained from the SPSA. We show that SPSA significantly improves the production plan by either searching for better CF parameters or by directly optimizing releases.
Bibliography:SourceType-Scholarly Journals-1
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content type line 14
ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2014.2303316