Journal
INFORMATION FUSION
Volume 43, Issue -, Pages 13-26Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.inffus.2017.11.008
Keywords
Multi-expert multi-criteria decision making; Quality function deployment; Probabilistic linguistic term set; Innovative product design selection; ORESTE; Shared cars
Funding
- National Natural Science Foundation of China [71501135, 71771156]
- China Postdoctoral Science Foundation [2016T90863, 2016M602698]
- Scientific Research Foundation for Excellent Young Scholars at Sichuan University [2016SCU04A23]
- International Visiting Program for Excellent Young Scholars of SCU
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The quality function deployment (QFD) is an effective tool to translate the customer requirements (CRs) to the design requirements (DRs) of a product. The process of selecting the optimal innovative product design to maximize customer satisfaction is full of uncertainty and fuzziness regarding to the users' preferences, the relationships between CRs and DRs and the merits of product designs. This study proposes a multi-expert multi criteria decision making method to solve the innovative product design selection problem by developing an enhanced QFD method combined with the complicated fuzzy linguistic representation model, the probabilistic linguistic term set (PLTS), and the ranking method, ORESTE. Firstly, we propose a probability aggregation method to integrate the individuals' subjective evaluations into group ones expressed as PLTSs. On this basis, we extend the QFD into the probabilistic linguistic context to get the DRs' fuzzy weights. Then, based on a new distance measure between PLTSs, a probabilistic linguistic global preference score function and three kinds of probabilistic linguistic preference intensity formulas are proposed. Furthermore, we develop a PL-ORESTE method to obtain the preference, indifference and incomparability relations between the alternatives. For the facility of application, we develop the procedure of the QFD-based PL-ORESTE method. Given that the shared cars is a new industry appeared in Chinese market, we finally illustrate the applicability of the proposed method by a case study concerning the selection of innovative designs of Panda shared cars.
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