New design concept evaluation method involving customer preferences based on rough distance to redefined ideal solution

Abstract

Currently, to get more popular design solution, customers are allowed to provide their preferences for new design concepts to be evaluated. Existing customer-involved design concept evaluation (CDCE) methods based on multi-criteria decision-making (MCDM) models collect customer preferences for certain pre-determined criteria as the one kind of evaluation factor without further exploring these subjective data, and sort the alternatives according to their deviations to positive ideal solution (PIS) and negative ideal solution (NIS). The elements of PIS and NIS are defined only limited to considering the benefit-like or cost-like characteristics of criteria without considering the impacts of customer preferences. To make better use of the customer preferences in CDCE, we propose a new multi-criteria CDCE method which not only captures the customer-related weights and importance ratings of criteria from customer preferences, but also more importantly, redefines PIS and NIS by comprehensive considering the characteristics of criteria and their importance ratings. Rough distances between alternatives and redefined ideal solutions are calculated to obtain the preferred degrees of alternatives for priority ranking, so the proposed method is named as evaluation based on rough distance to redefined ideal solution (RD-RIS). With the help of new combined ideal solution definition strategy adopted in RD-RIS, the best design concept recommended by RD-RIS is favored by customers as well as satisfies the constraints of evaluation criteria. Besides theoretical research, a practical example is introduced to illustrate the process of RD-RIS, and the empirical comparisons between RD-RIS and other classical MCDM methods are carried out to validate the superiority of RD-RIS. Experimental results show that RD-RIS is a feasible CDCE method which can give more reliable and reasonable evaluation results based on different customer preference values.