A constraint-based approach to feasibility assessment in preliminary design

In this paper, we present the development and application of a technical feasibility model used in preliminary design to determine whether a set of desired product specifications obtained from marketing is feasible in the engineering domain. This model is developed by integrating the capabilities of a multiobjective design problem, a multicriteria design optimization tool, a Pareto frontier gap analyzer, metamodeling methods, and use of the Pareto frontier as a constraint for feasibility assessment. Although the tools are independent of the domain, their application is illustrated using two examples: a simple three-objective mathematical problem and a five-objective passenger vehicle design problem. The feasibility of the desired product specifications is determined with respect to the problem's Pareto frontier, which is considered to be the necessary constraint to satisfy.

[1]  J. Dennis,et al.  A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems , 1997 .

[2]  Kevin Tucker,et al.  Response surface approximation of pareto optimal front in multi-objective optimization , 2004 .

[3]  Glynn J. Sundararaj,et al.  Ability of Objective Functions to Generate Points on Nonconvex Pareto Frontiers , 2000 .

[4]  Lee W. Jones,et al.  Invention and creativity in automated design by means of genetic programming , 2004, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[5]  Shapour Azarm,et al.  A MULTIOBJECTIVE INTERACTIVE SEQUENTIAL HYBRID OPTIMIZATION TECHNIQUE FOR DESIGN DECISION MAKING , 2000 .

[6]  Scott Ferguson,et al.  A study of convergence and mapping in preliminary vehicle design , 2005 .

[7]  Kemper Lewis,et al.  Effective generation of Pareto sets using genetic programming , 2001 .

[8]  Joachim Stender,et al.  Parallel Genetic Algorithms: Theory and Applications , 1993 .

[9]  Jongsoo Lee,et al.  Parallel Genetic Algorithm Implementation in Multidisciplinary Rotor Blade Design , 1996 .

[10]  Kemper Lewis,et al.  Multi-Attribute Decision Making Using Hypothetical Equivalents and Inequivalents , 2004 .

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  Zahed Siddique,et al.  A mass customization information framework for integration of customer in the configuration/design of a customized product , 2004, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[13]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[14]  Shapour Azarm,et al.  Constraint handling improvements for multiobjective genetic algorithms , 2002 .

[15]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[16]  Raymond M. Kolonay,et al.  A FEDERATED INTELLIGENT PRODUCT ENVIRONMENT , 2000 .

[17]  Kalyanmoy Deb,et al.  Real-coded Genetic Algorithms with Simulated Binary Crossover: Studies on Multimodal and Multiobjective Problems , 1995, Complex Syst..

[18]  Peter Fenyes,et al.  A New System for Multidisciplinary Analysis and Optimization of Vehicle Architectures , 2002 .

[19]  J. Koski.  Defectiveness of weighting method in multicriterion optimization of structures , 1985 .

[20]  Sophia Lefantzi,et al.  DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. , 2011 .

[21]  Rina Dechter,et al.  Advances in AND / OR Branch-and-Bound Search for Constraint Optimization , 2005 .

[22]  Kemper Lewis,et al.  An Approach to Facilitate Decision Tradeoffs in Pareto Solution Sets , 2000 .

[23]  David E. Goldberg,et al.  Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise , 1996, Evolutionary Computation.

[24]  M. Ehrgott.  Multiobjective Optimization , 2008, AI Mag..

[25]  J. C. Townsend,et al.  Integration of a CAD System Into an MDO Framework , 1998 .

[26]  Christian Fonteix,et al.  Multicriteria optimization using a genetic algorithm for determining a Pareto set , 1996, Int. J. Syst. Sci..

[27]  Alan de Pennington,et al.  Constraint-based design using an operational approach , 1993 .

[28]  Carlos A. Coello Coello,et al.  Design of combinational logic circuits through an evolutionary multiobjective optimization approach , 2002, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[29]  Amadou Ndiaye,et al.  On the use of JAVA and RMI in the development of a computer framework for MDO , 2000 .

[30]  Brian C. Watson,et al.  AN INTERFACE BETWEEN SDRC I-DEAS AND THE GENESIS STRUCTURAL ANALYSIS AND OPTIMIZATION CODE , 2000 .

[31]  Shapour Azarm,et al.  Comparison of Two Multiobjective Optimization Techniques with and within Genetic Algorithms , 1999 .

[32]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[33]  Judy M. Vance,et al.  A computer tool to facilitate cross-attribute optimization , 1996 .

[34]  Achille Messac,et al.  Physical programming's ability to generate a well-distributed set of Pareto points , 2000 .

[35]  Michael S. Eldred,et al.  DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis Version 3.0 Reference Manual , 2001 .

[36]  Linda C. Schmidt,et al.  A Constraint Satisfaction Problem Approach Linking Function and Grammar-Based Design Generation to Assembly , 2005 .

[37]  Kemper Lewis,et al.  Robust multiattribute decision making under risk and uncertainty in engineering design , 2005 .