Rational decision making models with incomplete weight information for production line assessment

In the age of mass customization with service oriented manufacturing, More and more production lines are designed according to the frequently changing customer demands. In order to improve upon the flexible production lines, rapid assessment tools have to be developed and implemented. Since uncertain information and incomplete knowledge coexist inevitably in the rapid assessment process, in literature, fuzzy multi-attribute decision making models are established to handle them simultaneously. However, very few papers have paid adequate attention to the decision maker's rationality, i.e., the optimism level of the decision maker. Therefore, in this paper, a series of novel rational decision making models are developed in dealing with the multi-attribute assessment problems, which include incomplete preference information. And the evaluating data are expressed under interval-valued intuitionistic fuzzy settings. Compared with the traditional distance-based method, correlation coefficient between each alternative and the ideal solution with (alpha, beta) optimism level is applied. Furthermore, confidence level and cognitive dissonance of the decision maker are included in the overall decision making process in order to constitute a more comprehensive evaluation of the flexible production line. Besides, several insights are revealed concerning the levels of optimism and the weights of production line attributes. Finally, a three-stage empirical study is conducted to validate the proposed models, compare them with the traditional distance-based methods, and the result shows that our approach is not only valid but also outperforms the traditional methods. (C) 2012 Elsevier Inc. All rights reserved.