A global method for the limited K‐partitioning of hypergraphs representing optimal design problems in complex machine systems

Kybernetes ◽  
2010 ◽  
Vol 39 (6) ◽  
pp. 980-989 ◽  
Author(s):  
Li Shuiping ◽  
Wan Xiaoxue
Keyword(s):  
Optimal Design ◽  
Design Problems ◽  
Global Method ◽  
Machine Systems

Smart Optimization of Machine Systems Using Hierarchical Genotype Representations

1999 ◽  
Author(s):  
Masataka Yoshimura ◽  
Kazuhiro Izui
Keyword(s):  
Structural Design ◽  
Hierarchical Structures ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Problems ◽  
Design Variables ◽  
The Hierarchical Structure ◽  
Machine Systems ◽  
System Designs ◽  
Crossover And Mutation

Abstract Design problems for machine products are generally hierarchically expressed. With conventional product optimization methods, it is difficult to concurrently optimize all design variables of portions within the hierarchical structure. This paper proposes a design optimization method using genetic algorithms containing hierarchical genotype representations, so that the hierarchical structures of machine system designs are exactly expressed through genotype coding, and optimization can be concurrently conducted for all of the hierarchical structures. Crossover and mutation operations for manipulating the hierarchical genotype representations are also developed. The proposed method is applied to a machine-tool structural design to demonstrate its effectiveness.

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.

scholarly journals A Modular Framework for the Modelling and Optimization of Advanced Chromatographic Processes

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 65 ◽  
Author(s):  
Johannes Schmölder ◽  
Malte Kaspereit
Keyword(s):  
Optimal Design ◽  
Case Studies ◽  
Design Problems ◽  
Column Model ◽  
Current Implementation ◽  
Systematic Development ◽  
Model Equations ◽  
Modelling And Optimization ◽  
Modelling Simulation ◽  
Modular Framework

A framework is introduced for the systematic development of preparative chromatographic processes. It is intended for the optimal design of conventional and advanced concepts that exploit strategies, such as recycling, side streams, bypasses, using single or multiple columns, and combinations thereof. The Python-based platform simplifies the implementation of new processes and design problems by decoupling design tasks into individual modules for modelling, simulation, assertion of cyclic stationarity, product fractionation, and optimization. Interfaces to external libraries provide flexibility regarding the choice of column model, solver, and optimizer. The current implementation, named CADET-Process, uses the software CADET for solving the model equations. The structure of the framework is discussed and its application for optimal design of existing and identification of new chromatographic operating concepts is demonstrated by case studies.

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