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.

Mathematical Consideration on a Robustness Index for Adjustable Control Factors

2011 ◽  
Vol 338 ◽  
pp. 26-29
Author(s):  
Takeo Kato ◽  
Suguru Kimura ◽  
Yoshiyuki Matsuoka
Keyword(s):  
Product Performance ◽  
Design Solution ◽  
Design Parameters ◽  
User Needs ◽  
Design Problems ◽  
Calculating Method ◽  
Control Factors ◽  
The Monte Carlo Method ◽  
Robustness Index ◽  
Design Requirements

Due to increased diversity of user needs and market globalization, use application and environment of the products have diversified. This causes the products are difficult to be designed. In other words, it is difficult for designers to find a unique design parameter value (a unique design solution) which assures the fluctuant products performance to be satisfied the design requirements. In such cases, the designers derive several values (range) of design parameters which can be adjusted by users or manufacturers and enable to satisfy the product performance by the adjustment. Unfortunately, the kind of design problems have not discussed sufficiently in the conventional robust design researches. This paper describes a robustness index which is applicable to such design problems and a mathematical consideration on a calculating method of the index using the Monte Carlo method.

A Distributed Pool Architecture for Highly Constrained Optimization Problems in Complex Systems Design

2011 ◽  
Author(s):  
Vijitashwa Pandey ◽  
Zissimos P. Mourelatos
Keyword(s):  
Complex Systems ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Systems Design ◽  
Optimization Approach ◽  
Dynamic Changes ◽  
Design Problems ◽  
Complete Knowledge ◽  
Design Variables ◽  
Design Requirements

The design of complex systems design is challenging because of the presence of numerous design variables and constraints. Dynamic changes in design requirements and lack of complete knowledge of subsystem requirements add to the complexity. A recently proposed pool architecture has been shown to aide distributed solving of optimization problems. The approach not only saves solution time but also has other benefits like resiliency against failures of some processors. We apply this approach in this paper, to highly constrained design problems, with dynamically changing constraints, where finding a feasible solution is challenging. This task is distributed between the processors in the methodology we propose. We demonstrate the efficacy of our method using an MINLP-class of mechanical design optimization problem. We demonstrate the computational savings and the resistance to partial failures in the processors. In addition, we show how the optimization approach can adapt to dynamic changes in design constraints.

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.

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.

A Set-Based System for Eliminating Infeasible Designs in Engineering Problems Dominated by Uncertainty

1997 ◽  
Author(s):  
William W. Finch ◽  
Allen C. Ward
Keyword(s):  
Electronic Circuit ◽  
Predicate Logic ◽  
Software Tool ◽  
Design Parameters ◽  
Engineering Systems ◽  
Multiple Sources ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
Prototype Software

Abstract This paper gives an overview of a system which eliminates infeasible designs from engineering design problems dominated by multiple sources of uncertainty. It outlines methods for representing constraints on sets of values for design parameters using quantified relations, a special class of predicate logic expressions which express some of the causal information inherent in engineering systems. The paper extends constraint satisfaction techniques and describes elimination algorithms that operate on quantified relations and catalogs of toleranced or adjustable parts. It demonstrates the utility of these tools on a simple electronic circuit, and describes their implementation and test in a prototype software tool.

A Blackboard Architecture to Support Case-Based Reasoning in Mechanical Design

1992 ◽  
Author(s):  
Theodore Bardsz ◽  
Ibrahim Zeid
Keyword(s):  
Problem Solving ◽  
Engineering Design ◽  
Mechanical Design ◽  
Maximum Amount ◽  
Design Solution ◽  
Case Based Reasoning ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Design Plan ◽  
Case Based

Abstract One of the most significant issues in applying case-based reasoning (CBR) to mechanical design is to integrate previously unrelated design plans towards the solution of a new design problem. The total design solution (the design plan structure) can be composed of both retrieved and dynamically generated design plans. The retrieved design plans must be mapped to fit the new design context, and the entire design plan structure must be evaluated. An architecture utilizing opportunistic problem solving in a blackboard environment is used to map and evaluate the design plan structure effectively and successfuly. The architecture has several assets when integrated into a CBR environment. First, the maximum amount of information related to the design is generated before any of the mapping problems are addressed. Second, mapping is preformed as just another action toward the evaluation of the design plan. Lastly, the architecture supports the inclusion of memory elements from the knowledge base in the design plan structure. The architecture is implemented using the GBB system. The architecture is part of a newly developed CBR System called DEJAVU. The paper describes DEJAVU and the architecture. An example is also included to illustrate the use of DEJAVU to solve engineering design problems.

Towards a Standardized Engineering Framework for Distributed, Collaborative Product Realization

2003 ◽  
Author(s):  
Hae-Jin Choi ◽  
Jitesh H. Panchal ◽  
Janet K. Allen ◽  
David Rosen ◽  
Farrokh Mistree
Keyword(s):  
Industrial Product ◽  
The Internet ◽  
Engineering Systems ◽  
Computer Based ◽  
Design Requirements ◽  
Product Realization ◽  
Future Work

In this paper, we propose a standardized computer-based engineering framework to support distributed product realization. The requirements for a standardized distributed product realization framework are developed based on the Open Engineering Systems paradigm. Existing computer frameworks are evaluated against the requirements and the missing features are identified. Our efforts towards development of such a framework — eXtensible Distributed Product Realization (X-DPR) environment are discussed. X-DPR is flexible and applicable to general industrial product realization processes. It is used to integrate distributed, collaborative product realization activities over the Internet. We trace the development of the framework based on design requirements. Features of X-DPR are implemented to satisfy each requirement. X-DPR is compared to existing engineering frameworks based on the required features. Limitations and future work are presented.

A Proposal of Classification and Guideline of Selection for Design Modeling Methods

2007 ◽  
Author(s):  
Takashi Asanuma ◽  
Jumpei Kawashima ◽  
Yoshiki Ujiie ◽  
Yoshiyuki Matsuoka
Keyword(s):  
Design Process ◽  
Design Solution ◽  
Human Beings ◽  
Design Problems ◽  
Modeling Methods ◽  
Complex Design ◽  
The Social ◽  
Selection For ◽  
Design Objects ◽  
Exact Design

In recent years the demands of users and the social problems have been diverse. In design, the diverse demands of users and problems of the society have created increasingly complex design problems. Therefore, it is important to understand values and images of the design objects and analyze the relation among design objects, human beings and its environment to respond to the complicated design problems. A number of design modeling methods that realize above points have been proposed. Consequently, it is necessary for designers and engineers to derivate the exact design solution that responds to the complicated design problems. However, the framework of design modeling methods in design has not been established. Moreover, most of the current studies on the methods only respond to the problems in each aspect of design [1]. Therefore, designers and engineers apply the design modeling methods in each design process based on their knowledge and experiences. The guideline of selection for the application of design modeling methods has not been shown. Consequently, the guideline for selecting the design modeling methods is needed for designers and engineers to apply the methods appropriately in design.

An Approach to Improved Decentralized Design: The Modified Approximation-Based Decentralized Design (MADD) Framework

2007 ◽  
Author(s):  
Ashwin P. Gurnani ◽  
Kemper Lewis
Keyword(s):  
Nash Equilibrium ◽  
Bounded Rationality ◽  
Large Scale ◽  
Optimization Techniques ◽  
Objective Functions ◽  
Design Problems ◽  
Decentralized Design ◽  
The Impact ◽  
Rational Reaction Set

The design of large scale complex engineering systems requires interaction and communication between multiple disciplines and decentralized subsystems. One common fundamental assumption in decentralized design is that the individual subsystems only exchange design variable information and do not share objective functions or gradients. This is because the decentralized subsystems can either not share this information due to geographical constraints or choose not to share it due to corporate secrecy issues. Game theory has been used to model the interactions between distributed design subsystems and predict convergence and equilibrium solutions. These game theoretic models assume that designers make perfectly rational decisions by selecting solutions from their Rational Reaction Set (RRS), resulting in a Nash Equilibrium solution. However, empirical studies reject the claim that decision makers always make rational choices and the concept of Bounded Rationality is used to explain such behavior. In this paper, a framework is proposed that uses the idea of bounded rationality in conjunction with set-based design, metamodeling and multiobjective optimization techniques to improve solutions for convergent decentralized design problems. Through the use of this framework, entitled Modified Approximation-based Decentralized Design (MADD) framework, convergent decentralized design problems converge to solutions that are superior to the Nash equilibrium. A two subsystem mathematical problem is used as case study and simulation techniques are used to study the impact of the framework parameters on the final solution. The discipline specific objective functions within the case study problem are unconstrained and continuous — however, the implementation of the MADD framework is not restricted to such problems.

Scalable Set-Based Design Optimization and Remanufacturing for Meeting Changing Requirements

2020 ◽  
Author(s):  
Khalil Al Handawi ◽  
Petter Andersson ◽  
Massimo Panarotto ◽  
Ola Isaksson ◽  
Michael Kokkolaras
Keyword(s):  
Design Optimization ◽  
Optimal Designs ◽  
Design Problems ◽  
Direct Energy Deposition ◽  
Engineering Design Problems ◽  
Direct Energy ◽  
Transition Rules ◽  
Set Based Design ◽  
Manufacturing Technologies ◽  
Process Solutions

Abstract Engineering design problems often have open-ended requirements, especially in the early stages of development. Set-based design is a paradigm for exploring, and keeping under consideration, several alternatives so that commitment to a single design can be delayed until requirements are settled. In addition, requirements may change over the lifetime of a component or a system. Novel manufacturing technologies enable designs to be remanufactured to meet changed requirements. By considering this capability during the set-based design optimization process, solutions can be scaled to meet evolving requirements and customer specifications even after commitment. Such an ability can also support a circular economy paradigm based on the return of used or discarded components and systems to working condition. We propose a set-based design methodology to obtain scalable optimal solutions that can satisfy changing requirements through remanufacturing. We first use design optimization and surrogate modeling to obtain parametric optimal designs. This set of parametric optimal designs is then reduced to scalable optimal designs by observing a set of transition rules for the manufacturing process used (additive or subtractive). The methodology is demonstrated by means of a structural aeroengine component that is remanufactured by direct energy deposition of a stiffener to meet higher loading requirements.

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