Solving complex design problems through multiobjective optimisation taking into account judgements of users

Abstract In the design process of products or systems, a current trend consists in taking into account judgments of users. In this context, a multiobjective optimisation method taking into account judgments of a panel of subjects is proposed. It is aimed at identifying the best trade-offs between quantitative objectives and judgments of users. The method is divided in two steps: (1) judgment data acquisition and (2) integration of the judgment data into the multiobjective optimisation process. The method is based on a stochastic Pareto-based evolutionary algorithm for optimisation and on a multilinear interpolation for judgment modelling. The combination of these techniques makes it possible to solve complex problems, with up to eight decision variables and up to at least eight objectives. Relevant applications of the method include optimisation with judgments about various aspects of the product or system, identification of the best trade-offs satisfying at the same time several groups with different judgments, and analysis of the interest of market segmentation. For illustration purpose, a pilot study about an individual office lighting design problem is processed.

[1]  David E. Bell,et al.  Multiattribute Utility Functions: Decompositions Using Interpolation , 1979 .

[2]  Ashutosh Tiwari,et al.  Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms , 2008, IEEE Transactions on Evolutionary Computation.

[3]  A Parducci,et al.  The category effect with rating scales: number of categories, number of stimuli, and method of presentation. , 1986, Journal of experimental psychology. Human perception and performance.

[4]  Gideon Avigad,et al.  The multi-single-objective problem and its solution by way of evolutionary algorithms , 2011 .

[5]  L. Thurstone.  A law of comparative judgment. , 1994 .

[6]  Ian Wakeling,et al.  Balanced incomplete block designs useful for consumer experimentation , 2001 .

[7]  Hideyuki Takagi,et al.  New IEC research and frameworks , 2009 .

[8]  Kalyanmoy Deb,et al.  Multiobjective optimization , 1997 .

[9]  Ashutosh Tiwari,et al.  An interactive genetic algorithm-based framework for handling qualitative criteria in design optimization , 2007, Comput. Ind..

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

[11]  A Gordon,et al.  Classification, 2nd Edition , 1999 .

[12]  Ralph L. Keeney,et al.  Decisions with multiple objectives: preferences and value tradeoffs , 1976 .

[13]  Kazumi Yamakawa,et al.  A Study on the Practical Use of a Task and Ambient Lighting System in an Office , 2000 .

[14]  Panos Y. Papalambros,et al.  Incorporating user shape preference in engineering design optimisation , 2011 .

[15]  Hisao Ishibuchi,et al.  Evolutionary many-objective optimization: A short review , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[16]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[17]  Christopher A. Mattson,et al.  Usage scenarios for design space exploration with a dynamic multiobjective optimization formulation , 2013 .

[18]  Rodolphe Palm,et al.  Utilisation du bootstrap pour les problèmes statistiques liés à l'estimation des paramètres , 2002 .

[19]  Ian C. Parmee,et al.  Integrating aesthetic criteria with evolutionary processes in complex, free-form design – an initial investigation , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[20]  Hideyuki Takagi,et al.  Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation , 2001, Proc. IEEE.

[21]  Karl T. Ulrich,et al.  Augmenting the house of quality with engineering models , 2018 .

[22]  Joel Huber,et al.  Ideal Point Models of Preference , 1976 .

[23]  K. D. Tocher.  The Design and Analysis of Block Experiments , 1952 .

[24]  M. Inoue,et al.  Layout algorithm for an EC-based room layout planning support system , 2008, 2008 IEEE Conference on Soft Computing in Industrial Applications.

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

[26]  Hajime Kita,et al.  Integration of multi-objective and interactive genetic algorithms and its application to animation design , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[27]  Mitsuo Nagamachi,et al.  Kansei Engineering: A new ergonomic consumer-oriented technology for product development , 1995 .

[28]  NYNKE DE JONG,et al.  Effect of Sucrose in Breakfast Items on Pleasantness and Food Intake in The Elderly , 1996, Physiology & Behavior.

[29]  Emilie Poirson,et al.  Interactive user tests to enhance innovation , 2010 .

[30]  Yili Liu,et al.  The aesthetic and the ethic dimensions of human factors and design , 2003, Ergonomics.

[31]  Juyun Lim,et al.  Hedonic scaling: A review of methods and theory , 2011 .

[32]  Elizabeth H. Zandstra,et al.  Laboratory hedonic ratings as predictors of consumption , 1999 .

[33]  Michael H. Birnbaum,et al.  Using contextual effects to derive psychophysical scales , 1974 .

[34]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[35]  Jean-François Petiot,et al.  Product design: a vectors field-based approach for preference modelling , 2006 .

[36]  Jonathan Cagan,et al.  Quantifying Aesthetic Form Preference in a Utility Function , 2009 .

[37]  Ibo van de Poel,et al.  Methodological problems in QFD and directions for future development , 2007 .

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

[39]  Azahar T. Machwe,et al.  Integrating Aesthetic Criteria with a User-centric Evolutionary System via a Component-based Design Representation , 2005 .

[40]  Philipp Geyer,et al.  Multidisciplinary grammars supporting design optimization of buildings , 2008 .

[41]  Richard Y. K. Fung,et al.  Modelling of quality function deployment planning with resource allocation , 2003 .

[42]  C. de Graaf,et al.  Psychophysical and psychohedonic functions of four common food flavours in elderly subjects. , 1996, Chemical senses.

[43]  Seung Ki Moon,et al.  Platform design variable identification for a product family using multi-objective particle swarm optimization , 2014 .

[44]  Peter Boyce,et al.  Lighting quality and office work: two field simulation experiments , 2006 .