Optimal design problems of highly reliable systems and their solution methods by surrogate constraints algorithm

1988 ◽  
Vol 71 (10) ◽  
pp. 1-12
Author(s):  
Mitsunori Hikita ◽  
Yuji Nakagawa ◽  
Kyoichi Nakashima ◽  
Kazuharu Yamato
Keyword(s):  
Optimal Design ◽  
Surrogate Constraints ◽  
Design Problems ◽  
Reliable Systems ◽  
Solution Methods

An Adaptive Sequential Linear Programming Algorithm for Optimal Design Problems With Probabilistic Constraints

2005 ◽  
Author(s):  
Kuei-Yuan Chan ◽  
Steven J. Skerlos ◽  
Panos Y. Papalambros
Keyword(s):  
Linear Programming ◽  
Optimal Design ◽  
Probabilistic Constraints ◽  
Programming Algorithm ◽  
Sequential Linear Programming ◽  
Design Problems ◽  
Reliability Based Design ◽  
Balance Accuracy ◽  
New Strategy ◽  
Solution Methods

Optimal design problems with probabilistic constraints, often referred to as Reliability-Based Design Optimization (RBDO) problems, have been the subject of extensive recent studies. Solution methods to date have focused more on improving efficiency rather than accuracy and the global convergence behavior of the solution. A new strategy utilizing an adaptive sequential linear programming (SLP) algorithm is proposed as a promising approach to balance accuracy, efficiency, and convergence. The strategy transforms the nonlinear probabilistic constraints into equivalent deterministic ones using both first order and second order approximations, and applies a filter-based SLP algorithm to reach the optimum. Simple numerical examples show promise for increased accuracy without sacrificing efficiency.

An Adaptive Sequential Linear Programming Algorithm for Optimal Design Problems With Probabilistic Constraints

2006 ◽  
Vol 129 (2) ◽  
pp. 140-149 ◽  
Author(s):  
Kuei-Yuan Chan ◽  
Steven J. Skerlos ◽  
Panos Papalambros
Keyword(s):  
Linear Programming ◽  
Optimal Design ◽  
Optimization Problems ◽  
Probabilistic Constraints ◽  
Programming Algorithm ◽  
Sequential Linear Programming ◽  
Design Problems ◽  
Reliability Based Design ◽  
New Strategy ◽  
Solution Methods

Optimal design problems with probabilistic constraints, often referred to as reliability-based design optimization problems, have been the subject of extensive recent studies. Solution methods to date have focused more on improving efficiency rather than accuracy and the global convergence behavior of the solution. A new strategy utilizing an adaptive sequential linear programming (SLP) algorithm is proposed as a promising approach to balance accuracy, efficiency, and convergence. The strategy transforms the nonlinear probabilistic constraints into equivalent deterministic ones using both first order and second order approximations, and applies a filter-based SLP algorithm to reach the optimum. Simple numerical examples show promise for increased accuracy without sacrificing efficiency.

Optimal Design of a Gas Turbine Cogeneration Plant by a Hierarchical Optimization Method With Parallel Computing

2018 ◽  
Author(s):  
Ryohei Yokoyama ◽  
Yuji Shinano ◽  
Yuki Wakayama ◽  
Tetsuya Wakui
Keyword(s):  
Optimal Design ◽  
Energy Supply ◽  
Optimization Method ◽  
Optimal Operation ◽  
Hierarchical Optimization ◽  
Cogeneration Plant ◽  
Design Problems ◽  
Computation Efficiency ◽  
Supply Systems ◽  
Energy Supply Systems

To attain the highest performance of energy supply systems, it is necessary to rationally determine types, capacities, and numbers of equipment in consideration of their operational strategies corresponding to seasonal and hourly variations in energy demands. Mixed-integer linear programming (MILP) approaches have been applied widely to such optimal design problems. The authors have proposed a MILP method utilizing the hierarchical relationship between design and operation variables to solve the optimal design problems of energy supply systems efficiently. In addition, some strategies to enhance the computation efficiency have been adopted: bounding procedures at both the levels and ordering of the optimal operation problems at the lower level. In this paper, as an additional strategy to enhance the computation efficiency, parallel computing is adopted to solve multiple optimal operation problems in parallel at the lower level. In addition, the effectiveness of each and combinations of the strategies adopted previously and newly is investigated. This hierarchical optimization method is applied to an optimal design of a gas turbine cogeneration plant, and its validity and effectiveness are clarified through some case studies.

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