Name
Abstract | ||
|---|---|---|
The capability of modeling real-world system operations has turned simulation into an indispensable problem-solving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose.
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Year | DOI | Venue |
|---|---|---|
2017 | 10.5555/3242181.3242466 | WSC '17: Winter Simulation Conference
Las Vegas
Nevada
December, 2017 |
Field | DocType | ISSN |
Data modeling,Simulation design,Systems engineering,Numerical models,Computer science,Systems design,Stochastic process,Ranging,Statistical learning,Uncertain systems | Conference | 0891-7736 |
ISBN | Citations | PageRank |
978-1-5386-3427-1 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bahar Biller | 1 | 452 | 72.34 |
| Stephan Biller | 2 | 133 | 16.24 |
| Onur Dulgeroglu | 3 | 3 | 0.71 |
| Canan G. Corlu | 4 | 30 | 6.12 |