Name
Abstract | ||
|---|---|---|
The goal of this paper is to address the problem of evaluating the performance of a system running under unknown values for its stochastic parameters. A new approach called , based on simulation and classification software, is presented. It uses a number of simulations with very few replications and records the mean value of directly measurable quantities (called observables). These observables are used as input to a classification model that produces a prediction for the performance of the system. Application to an assemble-to-order system from the literature is described and detailed results illustrate the strength of the method. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s10479-009-0518-3 | Annals OR |
Keywords | Field | DocType |
Simulation-optimization,Logical analysis of data,Stochastic models | Mathematical optimization,Observable,Mean value,Measure (mathematics),Computer science,Logical analysis of data,Software,Stochastic modelling | Journal |
Volume | Issue | ISSN |
188 | 1 | 0254-5330 |
Citations | PageRank | References |
1 | 0.35 | 24 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miguel A. Lejeune | 1 | 253 | 21.95 |
| François Margot | 2 | 768 | 45.21 |