Visualization approaches for the prototype improvement problem

One of the basic engineering optimization problems is improvement of the prototype. This problem is often encountered by industrial and academic organizations that produce and design various objects (e.g. motor vehicles, machine tools, ships, and aircrafts). This paper presents an approach for improving the prototype by constructing the feasible and Pareto sets while performing multicriteria analysis. We introduce visualization methods that facilitate construction of the feasible and Pareto sets. Using these techniques developed on the basis of Parameter Space Investigation method, an expert can correctly state and solve the problem under consideration in a series of dialogues with the computer. Finally, we present a case study of improving the ship prototype. Copyright r 2008 John Wiley & Sons, Ltd.

[1]  Kivanc A. Anil.  Multi-criteria analysis in naval ship design , 2005 .

[2]  J. Halton.  On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals , 1960 .

[3]  Manohar P. Kamat,et al.  Multicriteria Optimization In Engineering: A Tutorial And Survey , 1993 .

[4]  Issei Fujishiro,et al.  The elements of graphing data , 2005, The Visual Computer.

[5]  I. Sobol.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[6]  P. Slovic,et al.  The Construction of Preference: Index , 2006 .

[7]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[8]  H. Faure.  Discrépance de suites associées à un système de numération (en dimension s) , 1982 .

[9]  R. B. Statnikov,et al.  Use of Pτ-nets for the approximation of the Edgeworth-Pareto set in multicriteria optimization , 1996 .

[10]  William S. Cleveland.  The elements of graphing data , 1980 .

[11]  Vladimir M. Ozernoy,et al.  Multiple criteria decision making in the USSR: A survey , 1988 .

[12]  Roman B. Statnikov,et al.  Multicriteria Optimization and Engineering , 1995 .

[13]  S C Fung.  RESISTANCE AND POWERING PREDICTION FOR TRANSOM STERN HULL FORMS DURING EARLY STAGE SHIP DESIGN , 1992 .

[14]  Shapour Azarm,et al.  Metrics for Quality Assessment of a Multiobjective Design Optimization Solution Set , 2001 .

[15]  Roman B. Statnikov,et al.  Multicriteria Analysis in Engineering , 2002 .

[16]  P. Shiu,et al.  Geometric and analytic number theory , 1991 .

[17]  J. Hammersley.  MONTE CARLO METHODS FOR SOLVING MULTIVARIABLE PROBLEMS , 1960 .

[18]  Paul Slovic,et al.  The Construction of Preference: References , 2006 .

[19]  Giampiero Mastinu,et al.  Multi-objective optimal design of road vehicle sub-systems by means of global approximation , 2000 .

[20]  Roman B. Statnikov,et al.  Multicriteria analysis of real-life engineering optimization problems: statement and solution , 2005 .

[21]  Lauwerens Kuipers,et al.  Uniform distribution of sequences , 1974 .

[22]  Vladimir Dobrokhodov,et al.  MODELING AND SIMULATION FRAMEWORK FOR MULTI-OBJECTIVE IDENTIFICATION OF A GUIDED DESCENDING SYSTEM , 2003 .

[23]  Minghe Sun,et al.  The Parameter Space Investigation Method of Multiple Objective Nonlinear Programming: A Computational Investigation , 1995, Oper. Res..

[24]  H. Niederreiter,et al.  Statistical independence properties of pseudorandom vectors produced by matrix generators , 1990 .

[25]  Roman B. Statnikov,et al.  Multicriteria Parametrical Identification of the Parafoil- Load Delivery System , 2005 .

[26]  Alex Bordetsky,et al.  Multicriteria analysis tools in real-life problems , 2006, Comput. Math. Appl..