Abstract
| Original language | English |
|---|---|
| Place of Publication | Tilburg |
| Publisher | Operations research |
| Number of pages | 29 |
| Volume | 2009-82 |
| Publication status | Published - 2009 |
Publication series
| Name | CentER Discussion Paper |
|---|---|
| Volume | 2009-82 |
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Keywords
- Statistics
- Design of experiments
- Inventory-Production
- Simulation
- Decision analysis
Cite this
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Robust Optimization in Simulation : Taguchi and Krige Combined. / Dellino, G.; Kleijnen, Jack P.C.; Meloni, C.
Tilburg : Operations research, 2009. (CentER Discussion Paper; Vol. 2009-82).Research output: Working paper › Discussion paper › Other research output
TY - UNPB
T1 - Robust Optimization in Simulation
T2 - Taguchi and Krige Combined
AU - Dellino, G.
AU - Kleijnen, Jack P.C.
AU - Meloni, C.
N1 - Subsequently published in Informs Journal on Computing (2012) Pagination: 29
PY - 2009
Y1 - 2009
N2 - Optimization of simulated systems is the goal of many methods, but most methods as- sume known environments. We, however, develop a `robust' methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by Kriging. We illustrate the resulting methodology through classic Economic Order Quantity (EOQ) inventory models. Our results suggest that robust optimization requires order quantities that dier from the classic EOQ. We also compare our latest results with our previous results that do not use Kriging but Response Surface Methodology (RSM).
AB - Optimization of simulated systems is the goal of many methods, but most methods as- sume known environments. We, however, develop a `robust' methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by Kriging. We illustrate the resulting methodology through classic Economic Order Quantity (EOQ) inventory models. Our results suggest that robust optimization requires order quantities that dier from the classic EOQ. We also compare our latest results with our previous results that do not use Kriging but Response Surface Methodology (RSM).
KW - Statistics
KW - Design of experiments
KW - Inventory-Production
KW - Simulation
KW - Decision analysis
M3 - Discussion paper
VL - 2009-82
T3 - CentER Discussion Paper
BT - Robust Optimization in Simulation
PB - Operations research
CY - Tilburg
ER -