scholarly journals A Multi-Objective Robust Optimization Design for Grid Emergency Goods Distribution Under Mixed Uncertainty

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 61117-61129 ◽  
Author(s):  
Yiwen Jiang ◽  
Lee Li ◽  
Zhensheng Liu
Keyword(s):  
Robust Optimization ◽  
Optimization Design ◽  
Multi Objective ◽  
Robust Optimization Design

scholarly journals Multi-Objective Robust Optimization Using a Postoptimality Sensitivity Analysis Technique: Application to a Wind Turbine Design

2015 ◽  
Vol 137 (1) ◽  
Author(s):  
Weijun Wang ◽  
Stéphane Caro ◽  
Fouad Bennis ◽  
Ricardo Soto ◽  
Broderick Crawford
Keyword(s):  
Sensitivity Analysis ◽  
Robust Optimization ◽  
Wind Turbine ◽  
Optimization Design ◽  
Optimization Problems ◽  
Design Environment ◽  
Multi Objective ◽  
Pareto Ranking ◽  
Analysis Technique ◽  
Robust Optimization Design

Toward a multi-objective optimization robust problem, the variations in design variables (DVs) and design environment parameters (DEPs) include the small variations and the large variations. The former have small effect on the performance functions and/or the constraints, and the latter refer to the ones that have large effect on the performance functions and/or the constraints. The robustness of performance functions is discussed in this paper. A postoptimality sensitivity analysis technique for multi-objective robust optimization problems (MOROPs) is discussed, and two robustness indices (RIs) are introduced. The first one considers the robustness of the performance functions to small variations in the DVs and the DEPs. The second RI characterizes the robustness of the performance functions to large variations in the DEPs. It is based on the ability of a solution to maintain a good Pareto ranking for different DEPs due to large variations. The robustness of the solutions is treated as vectors in the robustness function space (RF-Space), which is defined by the two proposed RIs. As a result, the designer can compare the robustness of all Pareto optimal solutions and make a decision. Finally, two illustrative examples are given to highlight the contributions of this paper. The first example is about a numerical problem, whereas the second problem deals with the multi-objective robust optimization design of a floating wind turbine.

Robust optimization design for fatigue life

2011 ◽  
Vol 47 (10) ◽  
pp. 1186-1190 ◽  
Author(s):  
Feng Li ◽  
Guangwei Meng ◽  
Lirong Sha ◽  
Liming Zhou
Keyword(s):  
Fatigue Life ◽  
Robust Optimization ◽  
Optimization Design ◽  
Robust Optimization Design

Simulation and Robust Optimization Design for Electromagnetic Railgun Performance

2019 ◽  
Vol 47 (6) ◽  
pp. 2964-2970
Author(s):  
Xiaobing Shang ◽  
Tao Chao ◽  
Ping Ma ◽  
Ming Yang
Keyword(s):  
Robust Optimization ◽  
Optimization Design ◽  
Robust Optimization Design
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