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  • 1.
    Bandaru, Sunith
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Gaur, Abhinav
    Department of Electrical and Computer Engineering, Michigan State University, USA.
    Deb, Kalyanmoy
    Department of Electrical and Computer Engineering, Michigan State University, USA.
    Khare, Vineet
    Amazon Development Centre (India) Pvt. Ltd., Bengaluru, India.
    Chougule, Rahul
    Department of Mechanical Engineering, Walchand College of Engineering, Sangli, India.
    Bandyopadhyay, Pulak
    General Motors R&D Center, Warren, USA.
    Development, analysis and applications of a quantitative methodology for assessing customer satisfaction using evolutionary optimization2015In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 30, p. 265-278Article in journal (Refereed)
    Abstract [en]

    Consumer-oriented companies are getting increasingly more sensitive about customer's perception of their products, not only to get a feedback on their popularity, but also to improve the quality and service through a better understanding of design issues for further development. However, a consumer's perception is often qualitative and is achieved through third party surveys or the company's recording of after-sale feedback through explicit surveys or warranty based commitments. In this paper, we consider an automobile company's warranty records for different vehicle models and suggest a data mining procedure to assign a customer satisfaction index (CSI) to each vehicle model based on the perceived notion of the level of satisfaction of customers. Based on the developed CSI function, customers are then divided into satisfied and dissatisfied customer groups. The warranty data are then clustered separately for each group and analyzed to find possible causes (field failures) and their relative effects on customer's satisfaction (or dissatisfaction) for a vehicle model. Finally, speculative introspection has been made to identify the amount of improvement in CSI that can be achieved by the reduction of some critical field failures through better design practices. Thus, this paper shows how warranty data from customers can be utilized to have a better perception of ranking of a product compared to its competitors in the market and also to identify possible causes for making some customers dissatisfied and eventually to help percolate these issues at the design level. This closes the design cycle loop in which after a design is converted into a product, its perceived level of satisfaction by customers can also provide valuable information to help make the design better in an iterative manner. The proposed methodology is generic and novel, and can be applied to other consumer products as well.

  • 2.
    Deb, Kalyanmoy
    et al.
    Michigan State University.
    Bandaru, Sunith
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Greiner, David
    Universidad de Las Palmas de Gran Canaria.
    Gaspar-Cunha, António
    University of Minho, Campus de Azurém.
    Tutum, Cem Celal
    Michigan State University.
    An integrated approach to automated innovization for discovering useful design principles: Case studies from engineering2014In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 15, no 2, p. 42-56Article in journal (Refereed)
    Abstract [en]

    Computational optimization methods are most often used to find a single or multiple optimal or near-optimal solutions to the underlying optimization problem describing the problem at hand. In this paper, we elevate the use of optimization to a higher level in arriving at useful problem knowledge associated with the optimal or near-optimal solutions to a problem. In the proposed innovization process, first a set of trade-off optimal or near-optimal solutions are found using an evolutionary algorithm. Thereafter, the trade-off solutions are analyzed to decipher useful relationships among problem entities automatically so as to provide a better understanding of the problem to a designer or a practitioner. We provide an integrated algorithm for the innovization process and demonstrate the usefulness of the procedure to three real-world engineering design problems. New and innovative design principles obtained in each case should clearly motivate engineers and practitioners for its further application to more complex problems and its further development as a more efficient data analysis procedure.

  • 3.
    Syberfeldt, Anna
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Rogström, Joel
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    A two-step multi-objectivization method for improved evolutionary optimization of industrial problems2018In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 64, p. 331-340Article in journal (Refereed)
    Abstract [en]

    Multi-objectivization means that helper objectives are added to an optimization problem with the purpose of altering the search space in a way that improves the progress of the optimization algorithm. In this paper, a new method for multi-objectivization is proposed that is based on a two-step process. In the first step, a helper objective that conflicts with the main objective is added, and in the second step a helper objective that is in harmony with, but subservient to, the main objective is added. In contrast to existing methods for multi-objectivization, the proposed method aims at obtaining improved results in real-world optimizations by focusing on three aspects: (a) adding as little extra complexity to the problem as possible, (b) achieving an optimal balance between exploration and exploitation in order to promote an efficient search, and (c) ensuring that the main objective, which is of main interest to the user, is always prioritized. Results from evaluating the proposed method on a complex real-world scheduling problem and a theoretical benchmark problem show that the method outperforms both a traditional single-objective approach and the prevailing method for multi-objectivization. Besides describing the proposed method, the paper also outlines interesting aspects of multi-objectivization to investigate in the future.

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