A trade-off function to tackle robust design problems in engineering

In this paper, an original trade-off function is proposed to deal with robust design in engineering. Robust design in engineering aims at sizing systems with a low variation in all performance measures under uncertainties. It is mainly related to the generation and evaluation of candidate solutions. However, it is also suitable to consider the robustness in the decision-making process to ensure the selection of the most preferred design in the early stages of the design process. The purpose of our research work consists in tackling the robust design problem as a trade-off between two design objectives: (i) improve the global level of performance of the system and (ii) reduce the sensitivity of the performance with regard to uncertainty sources. The robustness of the candidate solutions is evaluated by a trade-off function modelling the designer's intention with an a priori articulation of preferences. Design objective formulation is based on the concept of desirability function and indices. Candidates solutions are ranked according to their ability of reducing the design sensitivity while remaining highly desirable. The developed approach is illustrated on an example of a truss structure design problem which is numerically solved by a genetic algorithm.

[1]  William Kendall,et al.  Robust Design , 2015 .

[2]  Raymond Chiong,et al.  Nature-Inspired Algorithms for Optimisation , 2009, Nature-Inspired Algorithms for Optimisation.

[3]  Kim Fung Man,et al.  Multiobjective Optimization , 2011, IEEE Microwave Magazine.

[4]  Deborah L Thurston,et al.  Utility Function Fundamentals , 2006 .

[5]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[6]  Bernadette Bouchon-Meunier,et al.  Logique floue : principes, aide à la décision , 2003 .

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

[8]  T. R. Bement,et al.  Taguchi techniques for quality engineering , 1995 .

[9]  Jasbir S. Arora,et al.  Survey of multi-objective optimization methods for engineering , 2004 .

[10]  Farrokh Mistree,et al.  A procedure for robust design: Minimizing variations caused by noise factors and control factors , 1996 .

[11]  Matthias Ehrgott,et al.  Multiple criteria decision analysis: state of the art surveys , 2005 .

[12]  Donald G. Saari Fundamentals and Implications of Decision-Making , 2006 .

[13]  Donald G. Saari,et al.  A chaotic Exploration of Aggregation Paradoxes , 1995, SIAM Rev..

[14]  Kwang-Jae Kim,et al.  Simultaneous optimization of mechanical properties of steel by maximizing exponential desirability functions , 2000 .

[15]  Erik K. Antonsson,et al.  Aggregation functions for engineering design trade-offs , 1995, Fuzzy Sets Syst..

[16]  George A. Hazelrigg,et al.  Validation of engineering design alternative selection methods , 2003 .

[17]  Johan Andersson,et al.  A survey of multiobjective optimization in engineering design , 2001 .

[18]  E. Antonsson,et al.  USING INDIFFERENCE POINTS IN ENGINEERING DECISIONS , 2000 .

[19]  Wei Chen,et al.  Decision Making in Engineering Design , 2006 .

[20]  Joaquim R. R. A. Martins,et al.  Multidisciplinary Design Optimization for Complex Engineered Systems: Report From a National Science Foundation Workshop , 2011 .

[21]  G. Derringer,et al.  Simultaneous Optimization of Several Response Variables , 1980 .

[22]  Erik K. Antonsson,et al.  Trade-off strategies in engineering design , 1991 .

[23]  Kristin L. Wood,et al.  Computations with Imprecise Parameters in Engineering Design: Background and Theory , 1989 .

[24]  Patrick Sebastian,et al.  Multi-objective optimization of the design of two-stage flash evaporators: Part 2. Multi-objective optimization , 2010 .