Modelling to generate alternatives (MGA)an approach to optimization aiming on providing not one, but more feasible solutions as different from each other as possible, all with the values of the objective function close to the optimal value (see e.g. Brill, Chang and Hopkins 1982 or Yeomans 2011).Kansei engineeringit is a consumer-oriented approach to product design based on the reflection of less tangible aspects such as feelings concerning the product in the design process. The aim is to inspire specific feelings by the features of the design alternative (see Nagamachi 1995;Jindo, Hirasago and Nagamachi 1995;or Kobayashi and Kinumura 2017).Kansei adjectives -Kansei words in the form of adjectives, i.e. words describing customers' or consumers' needs, feelings and perceptions concerning the product (see e.g. Jiao, Zhang and Helander (2006) for a Kansei mining system).Kansei taggroup or cluster of Kansei adjective corresponding to the same concept or basic emotion (Xu and Wunsch (2009) provide an example of a clustering algorithm suitable for the creation of Kanseiadjectives clusters, i.e. Kansei tags).Likert scalea psychometric measurement instrument popularized by Likert (see e.g. Likert ( 1932)) frequently used in questionnaires. Likert scales are discrete scales with linguistic labels on the agreedisagree or similar continuums, which are supposed to be symmetrical with respect to the middle point (either present in the scale itself, or theoretical; e.g. strongly agree, agree, undecided, disagree, strongly disagree) of the scale. Usually the equidistance of the scale-values is assumed.Semantic differentiala method proposed by Osgood, Suci and Tannenbaum (1957) for the measurement of attitudes. The method utilizes discrete bipolar-adjective scales to get input information and uses factor analysis to define the semantic space and represents the attitude towards a concept (or its connotative meaning) as a point in this n-dimensional semantic space.
The paper discusses the possibilities of adapting the recently introduced interval-valued semantic differential method to the multiple-criteria decision-making and evaluation context. It focuses on the differences and common ground of the intended use of the original semantic differentiation method and general multiple-criteria evaluation problems. The paper identifies the aspects of the interval-valued modification of the method that can be useful in multiple-criteria evaluation and also aspects that can be beneficial in the multi-expert evaluation setting and also possible limitations stemming from the transition to the multiple-criteria (or multi-expert) evaluation context. Finally the paper suggests potential application areas for the (intervalvalued) semantic differential based methods.
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