Book contents
- Frontmatter
- Contents
- Associated Links
- Preface
- Section 1 Getting Oriented
- Section 2 Harvesting Intelligence
- Section 3 Leveraging Dynamic Analysis
- 8 Controlled Simulation Analysis
- 9 Simulation Search, Optimization, and Reporting
- 10 Visualizing Complex Analytical Dynamics
- Section 4 Advanced Automation and Interfacing
- Glossary of Key Terms
- Appendix: Shortcut (Hot Key) Reference
- Index
9 - Simulation Search, Optimization, and Reporting
Published online by Cambridge University Press: 05 August 2013
- Frontmatter
- Contents
- Associated Links
- Preface
- Section 1 Getting Oriented
- Section 2 Harvesting Intelligence
- Section 3 Leveraging Dynamic Analysis
- 8 Controlled Simulation Analysis
- 9 Simulation Search, Optimization, and Reporting
- 10 Visualizing Complex Analytical Dynamics
- Section 4 Advanced Automation and Interfacing
- Glossary of Key Terms
- Appendix: Shortcut (Hot Key) Reference
- Index
Summary
A natural extension of a discussion of simulation, given our existing understanding of optimization, is how the two methods can be used together. The basic question behind simulation optimization is:
What decision (if any) tends to provide relatively superior results regardless of the uncertainty associated with the real-world problems they are designed to resolve?
Simulation provides the means by which to incorporate uncertainty into the evaluation of a specific decision or a predetermined handful of such decisions; however, this question implies a much greater scope. It suggests a formal search for the best decision across a vast range of possible alternative decisions. For simulated variants, the term best takes into account not just the average or expected value of parameters describing the setting (as would be common in discrete optimization), but also the potentially extreme performance of outliers, be that good or bad. For system simulations, the best would necessarily need to further relate to performance as the result of a sequence of events where the interplay of initial guiding decisions, complicated by uncertainty, might be extremely difficult to assess without sufficient simulation runs. The follow-up question then is:
How can we integrate the techniques associated with simulation and optimization into a single solid mechanism for meaningful decision support?
- Type
- Chapter
- Information
- Excel Basics to BlackbeltAn Accelerated Guide to Decision Support Designs, pp. 245 - 268Publisher: Cambridge University PressPrint publication year: 2013