Rough set and PSO-based ANFIS approaches to modeling customer satisfaction for affective product design |
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| Affiliation: | 1. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China;2. School of Design, The Hong Kong Polytechnic University, Hong Kong, China;3. Institute of Mechanical and Manufacturing, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK;1. Research Scholar, Mechanical Engineering Department, Birla Institute of Technology & Science Pilani, Pilani Campus (Rajasthan)-333 031, INDIA;2. Associate Professor, Mechanical Engineering Department, Birla Institute of Technology & Science Pilani, Pilani Campus (Rajasthan)-333 031, INDIA;1. Université de Lorraine/ERPI (Equipe de Recherche des processus Innovatifs), 8, rue Bastien Lepage, 54010 Nancy Cedex, France;2. Paris Descartes University, LIPADE, (Laboratoire d''Informatique Paris Descartes), 45 rue des Saints Pères, 75270 Paris Cedex 06, France;1. The Hong Kong Polytechnic University, Hong Kong;2. The Education University of Hong Kong, Hong Kong;1. Airbus Group. Carretera A8010, km.4, 41020 Sevilla, Spain;2. The Boeing Company. M/S 723-02. PO Box 3707 Seattle, WA 98124-2207;3. Department of Mechanical Eng., University Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, Spain;1. The Hong Kong Polytechnic University, School of Design, Hong Kong, Harbin Institute of Technology, Shenzhen Graduate School, China;2. The Hong Kong Polytechnic University, School of Design, Hong Kong;3. Harbin Institute of Technology, Shenzhen Graduate School, China |
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| Abstract: | Facing fierce competition in marketplaces, companies try to determine the optimal settings of design attribute of new products from which the best customer satisfaction can be obtained. To determine the settings, customer satisfaction models relating affective responses of customers to design attributes have to be first developed. Adaptive neuro-fuzzy inference systems (ANFIS) was attempted in previous research and shown to be an effective approach to address the fuzziness of survey data and nonlinearity in modeling customer satisfaction for affective design. However, ANFIS is incapable of modeling the relationships that involve a number of inputs which may cause the failure of the training process of ANFIS and lead to the ‘out of memory’ error. To overcome the limitation, in this paper, rough set (RS) and particle swarm optimization (PSO) based-ANFIS approaches are proposed to model customer satisfaction for affective design and further improve the modeling accuracy. In the approaches, the RS theory is adopted to extract significant design attributes as the inputs of ANFIS and PSO is employed to determine the parameter settings of an ANFIS from which explicit customer satisfaction models with better modeling accuracy can be generated. A case study of affective design of mobile phones is used to illustrate the proposed approaches. The modeling results based on the proposed approaches are compared with those based on ANFIS, fuzzy least-squares regression (FLSR), fuzzy regression (FR), and genetic programming-based fuzzy regression (GP-FR). Results of the training and validation tests show that the proposed approaches perform better than the others in terms of training and validation errors. |
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| Keywords: | Affective product design Customer satisfaction Rough set theory Particle swarm optimization ANFIS |
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