Failure mode and effects analysis (FMEA) is a widely used tool of risk assessment to identify and eliminate failures of products and systems. However, the conventional FMEA has some defects, such as the same importance of risk factors, utilizing crisp numbers to evaluate failures without considering vagueness. Although fuzzy methods are used to improve the conventional FMEA, the fuzzy FMEA requires much priori information (e.g., fuzzy membership function), which cannot flexibly reflect the changes of a decision maker's preference. In addition, most of the previous methods suppose that decision makers are totally rational without considering their psychological factors. Actually, FMEA members’ judgments on the failure modes are often influenced by their bounded rationality. To solve those problems, an integrated FMEA model is proposed in this paper, which integrates the strength of variable precision rough set theory in handling vagueness and the merit of TODIM (an acronym in Portuguese of Interactive and Multicriteria Decision Making) approach in manipulating bounded rationality of decision makers. Finally, the proposed method is validated with a case study of a steam valve system to demonstrate its effectiveness and efficiency.

Quality function deployment (QFD) has been a popular product or service development method to convert customer needs (CNs) into technical requirements (TRs). Nevertheless, the traditional QFD has vagueness inherent in experts’ opinions in determining CNs’ weights and the relationship between CNs and TRs. In the previous research works, fuzzy set theory is popular to deal with imprecise information in QFD. However, it still suffers from several deficiencies. For example, it needs prior information that leads to relatively fixed intervals expressing vagueness; and it assumes the membership degrees are crisp. To solve the issues, this article develops an integrated QFD method, where rough sets and cloud model are utilized for treating uncertain information. The former expresses impreciseness without any other assumptions and the latter considers expert evaluations’ randomness. In order to obtain more comprehensive importance of CNs, a combination weighting method is utilized. In the end, a compressor rotor's industrial service design is conducted utilizing the proposed method, where its effectiveness is verified.

With the socio-economic development in China, the central government pays more and more attention to innovations. Universities, research institutes, and industries (URI) are the main bodies of innovation. Therefore, in order to perform sustainable development of innovation, it is essential for Chinese policy makers to better understand the relationship among URI innovations. In this paper, indicators for evaluating URI innovations are collected, which are classified into three subsystems. Utilizing regional data from 2009–2015 in China, the entropy method is subsequently used to examine the contribution of indicators to the relative innovation subsystems. Then, a coupling coordination degree model is developed based on the physical concept “coupling” to investigate the relationship of URI innovation subsystems in China. The results reveal that in the period of study: (1) research and development (R&D) input and output indicators are both important to the regional innovation levels of URI, namely, they are critical for improving the coupling coordination degree among the three innovation subsystems; (2) There is an increasing trend of the coupling coordination of URI innovation system in China; however, the gap between regions/provinces becomes larger due to different innovative resource endowments and socio-economic development conditions. The results shown in this paper can provide important implications for policy makers to obtain the sustainable development of URI innovation subsystems in China.