Research Article | Open Access
An Approach to Evaluate the Clothing Creative Design with Dual Hesitant Fuzzy Information
The problem of evaluating the clothing creative design with dual hesitant fuzzy information is the multiple attribute decision making problem. In this paper, we have utilized dual hesitant fuzzy hybrid average (DHFHA) operator to develop the model to solve the multiple attribute decision making problems for evaluating the clothing creative design. Finally, a practical example for evaluating the clothing creative design is given to verify the developed approach.
In the context of innovation-driven reformation and development of fashion industry in China, it becomes the most essential issue to enhance the ability of independent R&D and creative design level for Chinese local fashion brands. In quite a long period, fashion is considered to be determined by fashion designers [1, 2]. However, fashion is hereby considered to be formed according to certain social background, instead of being determined by certain people’s subjective minds. So fashion could be generated by precise analysis from objective factors. Now in the context of fast fashion, fashion design does not merely rely on the designers’ creativity, but all kinds of modern information technology are applied in the process of fashion design [3–5]. According to the characteristics of the fashion data warehouse system, an overall structure composed of fashion data dictionary, fashion data sources, fashion data management, fashion data mining, and the front-end decision support is formed. The proposed concept of Fashion Data Dictionary (FDD), including Fashion Color Data Dictionary, Fashion Material Data Dictionary, Fashion Accessory Data Dictionary, Fashion Pattern Data Dictionary, Fashion Technique Data Dictionary, Fashion Style Data Dictionary, and Fashion Look Data Dictionary, is formed, in order that all kinds of fashion data from different sources are unified in format. Each data dictionary regulates its data type, level, content, and standard presentation [6, 7]. Sources of fashion data extraction are fashion clothing, social background, and art works. Fashion clothing data sources include fashion shows, fashion market, fashion brand advertisement, target consumer, fashion e-shop, and fashion and fabric exhibition. Social background data sources include politics, economy, environment, science and technology, sports, and lifestyle. Art works data sources include TV drama, art, design, music, performance art, and literature . The fashion data management is defined including fashion data extraction, naming method, conversion rules, and loading standard, so that the fashion data extracted from a variety of sources could be loaded in the fashion warehouse with standardized data format. Social background has an important impact on the formation of fashion style which is the consensus of the fashion industry, but the study of the relationship between the two has always been to stay in the sociology of qualitative research [9–11].
The problem of evaluating the clothing creative design with dual hesitant fuzzy information is the multiple attribute decision making problems. In this paper, we have utilized dual hesitant fuzzy hybrid average (DHFHA) operator to develop the model to solve the multiple attribute decision making problems for evaluating the clothing creative design. Finally, a practical example for evaluating the evaluating the clothing creative design is given to verify the developed approach.
Definition 1 (see ). Let be a fixed set; then a dual hesitant fuzzy set (DHFS) on is described as in which and are two sets of some values in , denoting the possible membership degrees and nonmembership degrees of the element to the set , respectively, with the conditions where , , , and for all .
In the following, Wang et al.  had developed some dual hesitant fuzzy arithmetic aggregation operator based on the operations of DHFEs.
Definition 2 (see ). Let be a collection of DHFEs; then their aggregated value by using the DHFWA operator is also a DHFE, and where is the weight vector of , and , .
Definition 3 (see ). Let be a collection of DHFEs; then their aggregated value by using the DHFOWA operator is also a DHFE, and where is a permutation of , such that for all , and is the aggregation-associated weight vector such that and .
Definition 4 (see ). Let be a collection of DHFEs; then their aggregated value by using the DHFHA operator is also a DHFE, and where is the associated weighting vector, with , , is the th largest element of the dual hesitant fuzzy arguments , is the weighting vector of dual hesitant fuzzy arguments (), with , , and is the balancing coefficient. In particular, if , then DHFHA is reduced to the dual hesitant fuzzy weighted average (DHFWA) operator; if , then DHFHA is reduced to the dual hesitant fuzzy ordered weighted average (DHFOWA) operator.
Definition 5 (see ). Let be any two DHFEs, the score function of , and the accuracy function of , where and are the numbers of the elements in and , respectively.
3. An Approach to Evaluate the Clothing Creative Design with Dual Hesitant Fuzzy Information
Let be a discrete set of alternatives, and let be the state of nature. Suppose that the decision matrix is the dual hesitant fuzzy decision matrix, where are in the form of DHFEs.
In the following, we apply the DHFHA operator to the MADM problems for evaluating the clothing creative design with dual hesitant fuzzy information.
Step 1. We utilize the decision information given in matrix , and the DHFHA operator to derive the overall preference values of the alternative .
Step 2. Calculate the scores of the overall dual hesitant fuzzy preference values .
Step 3. Rank all the alternatives and select the best one(s) in accordance with the scores .
Step 4. End.
4. Numerical Example
Thus, in this section we will present a numerical example for evaluating the clothing creative design with dual hesitant fuzzy information in order to illustrate the method proposed in this paper. There are five possible clothing creative design alternatives for four attributes . The four attributes include the fashion design style , the color of dress design , the fabrics of clothing design , and the design of comfort , respectively. In order to avoid influencing each other, the decision makers are required to evaluate five possible clothing creative design alternatives under the above four attributes in anonymity and the decision matrix is presented in Table 1.
In the following, we utilize the approach developed for evaluating the clothing creative design with dual hesitant fuzzy information.
We utilize the decision information given in matrix and the DHFHA operator to obtain the overall preference values of the clothing creative design alternatives and calculate the scores of the overall dual hesitant fuzzy values of the clothing creative design alternatives :
Then, we rank all the clothing creative design alternatives in accordance with the scores of the : , and thus the most desirable clothing creative design alternative is .
In this paper, we have utilized dual hesitant fuzzy hybrid average (DHFHA) operator to develop the model to solve the multiple attribute decision making problems for evaluating the clothing creative design. Finally, a practical example for evaluating the clothing creative design is given to verify the developed approach.
Conflict of Interests
The author declares that there is no conflict of interests regarding the publication of this paper.
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Copyright © 2014 Ya-Mei Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.