scholarly journals Characterization of the Transient Response of Coupled Optimization in Multidisciplinary Design

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Erich Devendorf ◽  
Kemper Lewis
Keyword(s):  
Optimization Problems ◽  
Initial Conditions ◽  
Systems Design ◽  
Discrete Systems ◽  
Product Development Process ◽  
Multidisciplinary Design ◽  
Distributed Design ◽  
Equilibrium Solutions ◽  
Acceptable Solution ◽  
Number Of Iterations

Time is an asset of critical importance in a multidisciplinary design process and it is desirable to reduce the amount of time spent designing products and systems. Design is an iterative activity and designers consume a significant portion of the product development process negotiating a mutually acceptable solution. The amount of time necessary to complete a design depends on the number and duration of design iterations. This paper focuses on accurately characterizing the number of iterations required for designers to converge to an equilibrium solution in distributed design processes. In distributed design, systems are decomposed into smaller, coupled design problems where individual designers have control over local design decisions and seek to achieve their own individual objectives. These smaller coupled design optimization problems can be modeled using coupled games and the number of iterations required to reach equilibrium solutions varies based on initial conditions and process architecture. In this paper, we leverage concepts from game theory, classical controls, and discrete systems theory to evaluate and approximate process architectures without carrying out any solution iterations. As a result, we develop an analogy between discrete decisions and a continuous time representation that we analyze using control theoretic techniques.

Quantifying the Convergence Time of Distributed Design Processes

2011 ◽  
Author(s):  
Erich Devendorf ◽  
Kemper Lewis
Keyword(s):  
Systems Design ◽  
Discrete Systems ◽  
Product Development Process ◽  
Convergence Time ◽  
Distributed Design ◽  
Equilibrium Solutions ◽  
Acceptable Solution ◽  
Design Processes ◽  
Process Architecture ◽  
Number Of Iterations

Time is an asset of critical importance in the design process and it is desirable to reduce the amount of time spent developing products and systems. Design is an iterative activity and a significant portion of time spent in the product development process is consumed by design engineers iterating towards a mutually acceptable solution. Therefore, the amount of time necessary to complete a design can be shortened by reducing the time required for design iterations or by reducing the number of iterations. The focus of this paper is on reducing the number of iterations required to converge to a mutually acceptable solution in distributed design processes. In distributed design, large systems are decomposed into smaller, coupled design problems where individual designers have control over local design decisions and seek to satisfy their own individual objectives. The number of iterations required to reach equilibrium solutions in distributed design processes can vary depending on the starting location and the chosen process architecture. We investigate the influence of process architecture on the convergence behavior of distributed design systems. This investigation leverages concepts from game theory, classical controls and discrete systems theory to develop a transient response model. As a result, we are able to evaluate process architectures without carrying out any solution iterations.

Multi-objective collaborative multidisciplinary design optimization using particle swarm techniques and fuzzy decision making

2012 ◽  
Vol 226 (9) ◽  
pp. 2281-2295 ◽  
Author(s):  
Mohammad Reza Farmani ◽  
Jafar Roshanian ◽  
Meisam Babaie ◽  
Parviz M Zadeh
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Fuzzy Decision ◽  
Swarm Optimization ◽  
Multi Objective

This article focuses on the efficient multi-objective particle swarm optimization algorithm to solve multidisciplinary design optimization problems. The objective is to extend the formulation of collaborative optimization which has been widely used to solve single-objective optimization problems. To examine the proposed structure, racecar design problem is taken as an example of application for three objective functions. In addition, a fuzzy decision maker is applied to select the best solution along the pareto front based on the defined criteria. The results are compared to the traditional optimization, and collaborative optimization formulations that do not use multi-objective particle swarm optimization. It is shown that the integration of multi-objective particle swarm optimization into collaborative optimization provides an efficient framework for design and analysis of hierarchical multidisciplinary design optimization problems.

Backward Mapping Methodology for Design Synthesis

1999 ◽  
Author(s):  
Stephen C.-Y. Lu ◽  
Satish T. S. Bukkapatnam ◽  
Ping Ge ◽  
Nanxin Wang
Keyword(s):  
Engineering Design ◽  
Design Space ◽  
Initial Conditions ◽  
Early Stage ◽  
Critical Role ◽  
Product Development Process ◽  
Engineering System ◽  
Design Synthesis ◽  
Performance Space ◽  
Backward Mapping

Abstract Design efficiency and robustness at early stage of parametric engineering design play a critical role in reducing cycle time and improving product quality in the overall product development process. Usually, the “forward mapping” approach, is used to find designs, where the desirable performances are satisfied through large iterations of analysis and evaluation from design space to performance space. However, these approaches are time-consuming and involve blind search if the engineering system simulation models and/or initial conditions are not appropriately selected. On the other hand, common “reverse engineering” methods use domain-specific assumptions and are not effective in generic scenarios where the presumed conditions are violated. In this paper, a Backward Mapping Methodology for Design Synthesis (BMDS) is presented that can help conduct design synthesis rapidly and robustly at early stage of parametric engineering design. BMDS is a surrogate model-based approach that combines the strengths of metamodeling and statistics. It can help designers explicitly identify the robust design solutions that satisfy the designer-specified performance requirements through a “backward mapping” from the performance space directly to the design space. Preliminary case studies show its effectiveness and potential to be used as a generic early stage parametric design synthesis methodology in the future.

Multiobjective Collaborative Optimization

1997 ◽  
Author(s):  
Ravindra V. Tappeta ◽  
John E. Renaud
Keyword(s):  
System Design ◽  
Systems Design ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Accurate Estimation ◽  
Successful Implementation ◽  
Design Environment ◽  
Comprehensive Overview ◽  
Design Problems ◽  
Multidisciplinary Systems

Abstract This investigation focuses on the development of modifications to the Collaborative Optimization (CO) approach to multidisciplinary systems design, that will provide solution capabilities for multiobjective problems. The primary goal of this research is to provide a comprehensive overview and development of mathematically rigorous optimization strategies for MultiObjective Collaborative Optimization (MOCO). Collaborative Optimization strategies provide design optimization capabilities to discipline designers within a multidisciplinary design environment. To date these CO strategies have primarily been applied to system design problems which have a single objective function. Recent investigations involving multidisciplinary design simulators have reported success in applying CO to multiobjective system design problems. In this research three MultiObjective Collaborative Optimization (MOCO) strategies are developed, reviewed and implemented in a comparative study. The goal of this effort is to provide an in depth comparison of different MOCO strategies available to system designers. Each of the three strategies makes use of parameter sensitivities within multilevel solution strategies. In implementation studies, each of the three MOCO strategies is effective in solving two multiobjective multidisciplinary systems design problems. Results indicate that these MOCO strategies require an accurate estimation of parameter sensitivities for successful implementation. In each of the three MOCO strategies these parameter sensitivities are obtained using post-optimality analysis techniques.

Parameter Selection for Swarm Intelligence Algorithms

2018 ◽  
Vol 9 (4) ◽  
pp. 1-20
Author(s):  
Breno A. M. Menezes ◽  
Fabian Wrede ◽  
Herbert Kuchen ◽  
Fernando B. Lima Neto
Keyword(s):  
Population Size ◽  
Swarm Intelligence ◽  
Execution Time ◽  
Optimization Problems ◽  
Parallel Implementation ◽  
Fish School ◽  
Size Number ◽  
Complex Optimization ◽  
Selection For ◽  
Number Of Iterations

Swarm intelligence (SI) algorithms are handy tools for solving complex optimization problems. When problems grow in size and complexity, an increase in population or number of iterations might be required in order to achieve a good solution. These adjustments also impact the execution time. This article investigates the trade-off involving population size, number of iterations and problem complexity, aiming to improve the efficiency of SI algorithms. Results based on a parallel implementation of Fish School Search show that increasing the population size is beneficial for finding good solutions. However, we observed an asymptotic behavior, i.e. increasing the population over a certain threshold only leads to slight improvements. Furthermore, the execution time was analyzed.

Innovations in Collaborative Web Design

2007 ◽  
pp. 148-164
Author(s):  
Madelon Evers
Keyword(s):  
Stakeholder Participation ◽  
Action Learning ◽  
Systems Design ◽  
Web Design ◽  
Interactive Systems ◽  
Multidisciplinary Design ◽  
Design Decision ◽  
Design Teams ◽  
New Knowledge ◽  
Design Projects

In this chapter we analyse the link between multidisciplinary design and team learning, which, we argue, need to be supported in equal measure during Web design projects. We introduce a new approach to collaborative Web design, called the “Design and Learning Methodology,” as a way to support these two processes. The approach involves many stakeholders, including future website users, in design decision-making. It structures stakeholder participation through multidisciplinary design teams (MDTs). It uses professional facilitators to guide design and learning processes. Facilitation tools are drawn from a combination of action learning methods, which help MDTs reflect and act on new knowledge gained from design experiences, and human-centred design, which is an international protocol for achieving quality in interactive systems design (ISO 9000 series). Based on our research, we describe how facilitation of the process of learning from design contributes to continuous improvement in collaborative competencies needed for Web design.

A Regularized Inexact Penalty Decomposition Algorithm for Multidisciplinary Design Optimization Problems With Complementarity Constraints

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
Shen Lu ◽  
Harrison M. Kim
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Original Problem ◽  
Equilibrium Problems ◽  
Optimization Problems ◽  
Decomposition Algorithm ◽  
Multidisciplinary Design ◽  
Stationary Points ◽  
Complementarity Constraints ◽  
Regularization Technique

Economic and physical considerations often lead to equilibrium problems in multidisciplinary design optimization (MDO), which can be captured by MDO problems with complementarity constraints (MDO-CC)—a newly emerging class of problem. Due to the ill-posedness associated with the complementarity constraints, many existing MDO methods may have numerical difficulties solving this class of problem. In this paper, we propose a new decomposition algorithm for the MDO-CC based on the regularization technique and inexact penalty decomposition. The algorithm is presented such that existing proofs can be extended, under certain assumptions, to show that it converges to stationary points of the original problem and that it converges locally at a superlinear rate. Numerical computation with an engineering design example and several analytical example problems shows promising results with convergence to the all-in-one solution.

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