| Edition |
1st ed. 2019. |
| Descript. |
XXV, 175 pages 62 illustrations, 42 illustrations in color. online resource. |
| Phys Desc |
text file PDF rda |
| Series |
Springer Theses, Recognizing Outstanding Ph.D. Research,
2190-5053
|
|
Springer Theses, Recognizing Outstanding Ph.D. Research, 2190-5053
|
| Contents |
Introduction -- Background -- Active Robust Optimization -- Active Robust Multi-Objective Optimization -- Case Studies -- Conclusions -- Appendix: Calculation of the q+ Indicator. |
| Summary |
This book presents a novel framework, known as Active Robust Optimization, which provides the tools for evaluating, comparing and optimizing changeable products. Since any product that can change its configuration during normal operation may be considered a changeable product, the framework is widely applicable. Further, the methodology enables designers to use adaptability to deal with uncertainties and so avoid over-conservative designs. Offering a comprehensive overview of the framework, including its unique features, such as its ability to optimally respond to uncertain situations, the book also defines a new class of optimization problem and examines the effects of changes in various parameters on their solution. Lastly, it discusses innovative approaches for solving the problem and demonstrates these with two examples from different fields in engineering design: optimization of an optical table and optimization of a gearbox. |
| Sys Details |
eBook access requires you to log in as a Federation University Australia library user |
| Notes |
Springer eBooks |
| Subject |
Mathematical optimization.
|
|
Artificial intelligence.
|
|
Manufactures.
|
| Other Author |
SpringerLink (Online service)
|
| ISBN |
9783030150501 |
| ISBN/ISSN |
10.1007/978-3-030-15050-1 doi |
|
