An Integrated Framework of Product Kansei Decision-Making Based on Hesitant Linguistic Fuzzy Term Sets

Kansei adjectives have the advantage of close to consumers’ perception of a product. But consumers may show hesitation and opinion discrepancy while expressing their preferences through comparative Kansei adjectives. To address this, this article investigates hesitant linguistic expression and its application in product Kansei decision-making. An integrated framework is firstly presented based on hesitant fuzzy linguistic term sets (HFLTSs), which involves a consensus model for assessing consistency of consumers’ preferences, particle swarm optimization (PSO) method for adjusting Kansei opinions when agreement fails, and the technique for order preference by similarity to an ideal solution (TOPSIS) for yielding ranked product solutions. An example of charging piles design was used to illustrate the necessity of considering consumers’ hesitation in Kansei decision-making. With the proposed method, the consensus level of consumers’ preferences is enhanced from 0.8339 to 0.9052, and the overall satisfaction degree is also improved. Furthermore, the results of Kansei decision-making through optimizing Kansei preferences are significantly different from that without optimization. This improvement demonstrates that hesitance and consensus change will influence design decision-making and they should be considered in product Kansei decision-making. The given example shows the validity and suitability of the proposed approach.