Convergence and Stability in Distributed Design of Large Systems

2004 ◽  
Author(s):  
Vincent Chanron ◽  
Kemper Lewis
Keyword(s):  
Optimization Problem ◽  
Equilibrium Points ◽  
Design Problems ◽  
Design Decisions ◽  
Convergence Conditions ◽  
Decentralized System ◽  
Complex Design ◽  
Large Systems ◽  
Decentralized Design ◽  
Standard Techniques

Decentralized systems constitute a special class of design under distributed environments. They are characterized as large and complex systems divided into several smaller entities that have autonomy in local optimization and decision-making. The mechanisms behind this network of decentralized design decisions create difficult management and coordination issues. Standard techniques to modeling and solving decentralized design problems typically fail to understand the underlying dynamics of the decentralized processes and therefore result in suboptimal solutions. This paper aims to model and understand the mechanisms and dynamics behind a decentralized set of decisions within a complex design process. This paper builds on already existing results of convergence in decentralized design for simple problems to extend them to any kind of quadratic decentralized system. This involves two major steps: developing the convergence conditions for the distributed optimization problem, and finding the equilibrium points of the design space. Illustrations of the results are given in the form of hypothetical decentralized examples.

Handling Multiple Objectives in Decentralized Design

2005 ◽  
Author(s):  
Vincent Chanron ◽  
Kemper Lewis ◽  
Yayoi Murase ◽  
Kazuhiro Izui ◽  
Shinji Nishiwaki ◽  
...  
Keyword(s):  
Complex Systems ◽  
Design Solution ◽  
Distributed Environments ◽  
Engineering Systems ◽  
Design Problems ◽  
Design Requirements ◽  
Decentralized Design ◽  
Set Based Design ◽  
Standard Techniques ◽  
Different Parts

Most complex systems, including engineering systems such as cars, airplanes, and satellites, are the results of the interactions of many distinct entities working on different parts of the design. Decentralized systems constitute a special class of design under distributed environments. They are characterized as large and complex systems divided into several smaller entities that have autonomy in local optimization and decision-making. A primary issue in decentralized design processes is to ensure that the designers that are involved in the process converge to a single design solution that is optimal and meets the design requirements, while being acceptable to all the participants. This is made difficult by the strong interdependencies between the designers, which are usually characteristic of such systems. This paper proposes a critical review of standard techniques to modeling and solving decentralized design problems, and shows mathematically the challenges created by having multiobjective subsystems. A method based on set-based design is then proposed to alleviate some of these challenging issues. An illustration of its applicability is given in the form of the design of a space satellite.

A Study of Convergence in Decentralized Design

2003 ◽  
Author(s):  
Vincent Chanron ◽  
Kemper Lewis
Keyword(s):  
Efficient Solutions ◽  
Engineering Systems ◽  
Efficient Design ◽  
Numerical Series ◽  
Design Problems ◽  
Customer Expectations ◽  
Complex Design ◽  
Complex Engineering ◽  
Decentralized Design ◽  
Market Pressures

The decomposition and coordination of decisions in the design of complex engineering systems is a great challenge. Companies who design these systems routinely allocate design responsibility of the various subsystems and components to different people, teams or even suppliers. The mechanisms behind this network of decentralized design decisions create difficult management and coordination issues. However, developing efficient design processes is paramount, especially with market pressures and customer expectations. Standard techniques to modeling and solving decentralized design problems typically fail to understand the underlying dynamics of the decentralized processes and therefore result in suboptimal solutions. This paper aims to model and understand the mechanisms and dynamics behind a decentralized set of decisions within a complex design process. By using concepts from the fields of mathematics and economics, including Game Theory and the Cobweb Model, we model a simple decentralized design problem and provide efficient solutions. This new approach uses numerical series and linear algebra as tools to determine conditions for convergence of such decentralized design problems. The goal of this paper is to establish the first steps towards understanding the mechanisms of decentralized decision processes. This includes two major steps: studying the convergence characteristics, and finding the final equilibrium solution of a decentralized problem. Illustrations of the developments are provided in the form of two decentralized design problems with different underlying behavior.

Multi-Objective Optimization of an Autonomous Underwater Vehicle

2009 ◽  
Vol 43 (2) ◽  
pp. 48-60 ◽  
Author(s):  
M. Martz ◽  
W. L. Neu
Keyword(s):  
Design Space ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Design Problems ◽  
Single Measure ◽  
Design Variables ◽  
Complex Design ◽  
Final Design ◽  
Undersea Vehicle ◽  
Hierarchy Process

AbstractThe design of complex systems involves a number of choices, the implications of which are interrelated. If these choices are made sequentially, each choice may limit the options available in subsequent choices. Early choices may unknowingly limit the effectiveness of a final design in this way. Only a formal process that considers all possible choices (and combinations of choices) can insure that the best option has been selected. Complex design problems may easily present a number of choices to evaluate that is prohibitive. Modern optimization algorithms attempt to navigate a multidimensional design space in search of an optimal combination of design variables. A design optimization process for an autonomous underwater vehicle is developed using a multiple objective genetic optimization algorithm that searches the design space, evaluating designs based on three measures of performance: cost, effectiveness, and risk. A synthesis model evaluates the characteristics of a design having any chosen combination of design variable values. The effectiveness determined by the synthesis model is based on nine attributes identified in the U.S. Navy’s Unmanned Undersea Vehicle Master Plan and four performance-based attributes calculated by the synthesis model. The analytical hierarchy process is used to synthesize these attributes into a single measure of effectiveness. The genetic algorithm generates a set of Pareto optimal, feasible designs from which a decision maker(s) can choose designs for further analysis.

A Genetic Algorithm for Scheduling and Decomposition of Multidisciplinary Design Problems

1995 ◽  
Author(s):  
Stephen S. Altus ◽  
Ilan M. Kroo ◽  
Peter J. Gage
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Heuristic Method ◽  
Merit Function ◽  
Multidisciplinary Design ◽  
Design Problems ◽  
Design Studies ◽  
Complex Design ◽  
Complex Engineering

Abstract Complex engineering studies typically involve hundreds of analysis routines and thousands of variables. The sequence of operations used to evaluate a design strongly affects the speed of each analysis cycle. This influence is particularly important when numerical optimization is used, because convergence generally requires many iterations. Moreover, it is common for disciplinary teams to work simultaneously on different aspects of a complex design. This practice requires decomposition of the analysis into subtasks, and the efficiency of the design process critically depends on the quality of the decomposition achieved. This paper describes the development of software to plan multidisciplinary design studies. A genetic algorithm is used, both to arrange analysis subroutines for efficient execution, and to decompose the task into subproblems. The new planning tool is compared with an existing heuristic method. It produces superior results when the same merit function is used, and it can readily address a wider range of planning objectives.

Qualitative Knowledge and Manufacturing Considerations in Multidisciplinary Structural Optimization of Hybrid Material Structures

2006 ◽  
Vol 10 ◽  
pp. 143-152 ◽  
Author(s):  
Martin Huber ◽  
Horst Baier
Keyword(s):  
Hybrid Material ◽  
Optimization Problem ◽  
Fuzzy Rule ◽  
Optimal Designs ◽  
Design Parameters ◽  
Optimization Approach ◽  
Structural Level ◽  
Design Problems ◽  
Qualitative Knowledge ◽  
Typical Design

An optimization approach is derived from typical design problems of hybrid material structures, which provides the engineer with optimal designs. Complex geometries, different materials and manufacturing aspects are handled as design parameters using a genetic algorithm. To take qualitative information into account, fuzzy rule based systems are utilized in order to consider all relevant aspects in the optimization problem. This paper shows results for optimization tasks on component and structural level.

Nonlinear Integer and Discrete Programming in Mechanical Design

1988 ◽  
Author(s):  
E. Sandgren
Keyword(s):  
Mechanical Design ◽  
General Purpose ◽  
Conceptual Level ◽  
Design Problems ◽  
Design Decisions ◽  
Design Variables ◽  
Nonlinear Mathematical Programming ◽  
Discrete Programming ◽  
Mathematical Programming Problems ◽  
Quadratic Programming Method

Abstract A general purpose algorithm for the solution of nonlinear mathematical programming problems containing integer, discrete, zero-one and continuous design variables is described. The algorithm implements a branch and bound procedure in conjunction with both an exterior penalty function and a quadratic programming method. Variable bounds are handled independently from the design constraints which removes the necessity to reformulate the problem at each branching node. Examples are presented to demonstrate the utility of the algorithm for solving design problems. The use of zero-one variables to represent design decisions in order to allow conceptual level design to be performed is demonstrated.

Understand Complex Design Problems Using Systems Thinking

2011 ◽  
pp. 379-393 ◽  
Author(s):  
Tao Huang ◽  
Eric E. Anderson
Keyword(s):  
Product Design ◽  
Systems Theory ◽  
Design Research ◽  
Current System ◽  
Design Problems ◽  
Evolving Systems ◽  
Complex Design ◽  
Research Problems ◽  
Dynamics Models ◽  
New System

This chapter provides a brief overview of systems theory and suggests that product designers could use systems theory and systems dynamics models to improve our understanding of complex Product Design research problems, to anticipate how and where changes in these dynamically evolving systems might occur and how they might interact with the current system to produce a new system with new behaviors, and to identify leverage points within the system where potential policy or design process changes might be introduced to produce effective solutions to these problems with minimum policy resistance. By investigating the current and future trends of the application of systems theory in Product Design research, this chapter invites multidisciplinary discussions of these topics.

scholarly journals Direct Tracking of the Pareto Front of a Multi-Objective Optimization Problem

2020 ◽  
Vol 8 (9) ◽  
pp. 699
Author(s):  
Daniele Peri
Keyword(s):  
Pareto Front ◽  
Optimization Problem ◽  
Ship Design ◽  
Multi Objective Optimization ◽  
Design Problems ◽  
Multi Objective ◽  
Regular Sampling ◽  
Normal Boundary ◽  
Direct Tracking ◽  
Local Sampling

In this paper, some methodologies aimed at the identification of the Pareto front of a multi-objective optimization problem are presented and applied. Three different approaches are presented: local sampling, Pareto front resampling and Normal Boundary Intersection (NBI). A first approximation of the Pareto front is obtained by a regular sampling of the design space, and then the Pareto front is improved and enriched using the other two above mentioned techniques. A detailed Pareto front is obtained for an optimization problem where algebraic objective functions are applied, also in comparison with standard techniques. Encouraging results are also obtained for two different ship design problems. The use of the algebraic functions allows for a comparison with the real Pareto front, correctly detected. The variety of the ship design problems allows for a generalization of the applicability of the methodology.

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