FMEA Using IVIFSs and MABAC Method and Its Application to Radiation Therapy
In this chapter, we aim to develop an integrated risk prioritization approach to improve the performance of FMEA by using interval-valued intuitionistic fuzzy sets (IVIFSs) and the multi-attributive border approximation area comparison (MABAC) method. Mor
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FMEA Using IVIFSs and MABAC Method and Its Application to Radiation Therapy
In this chapter, we aim to develop an integrated risk prioritization approach to improve the performance of FMEA by using interval-valued intuitionistic fuzzy sets (IVIFSs) and the multi-attributive border approximation area comparison (MABAC) method. Moreover, a linear programming model is developed to obtain the optimal weights of risk factors when the weight information is incompletely known a priori. Finally, a practical example in a radiation oncology setting is presented to illustrate the applicability and effectiveness of the presented FMEA, and the results show that the new integrated method offers a useful and reliable tool for rational criticality analysis.
6.1 Introduction FMEA is a systematic and prospective risk management technique for evaluating a system, design, process, or service to identify where and how it might fail and evaluate the effects of different failure modes in order to find the most important ones and take actions to eliminate or mitigate them (Stamatis 2003). It allows organizations or institutions to proactively prevent failures rather than react to them. As indicated by many academicians and practitioners (e.g., Braglia et al. 2003b; Chemweno et al. 2015; Chin et al. 2009a; Liu et al. 2014), FMEA is an easy-to-use analytic tool and offers a straightforward way to resolve complicated processes, based on the involvement of FMEA team members. In the process of FMEA, failure effects, causes of failure, and detection measures vis-à-vis each potential failure mode are first listed based on expert knowledge and relevant experience. Then, FMEA team must determine the assessments of the risk factors O, S, and D for the corresponding failure modes. For the detailed steps to complete an FMEA process, please see Liu (2016) and Stamatis (2003). Generally, FMEA can provide valuable information to risk analysts regarding a product, system, or service to assist in improving its performance and reliability. In practice, however, significant limitations of the conventional FMEA exist (Chin et al. 2009b; Ko 2013; Liu et al. 2013a; Huang et al. 2017; Hu et al. 2018; Liu et al. 2018b). Therefore, it is only with further evolvement and refinement that © Springer Nature Singapore Pte Ltd. 2019 H.-C. Liu, Improved FMEA Methods for Proactive Healthcare Risk Analysis, https://doi.org/10.1007/978-981-13-6366-5_6
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6 FMEA Using IVIFSs and MABAC Method and Its Application …
FMEA can live up its potential and become a distinctive technique for system safety and reliability analysis. To overcome the inherent deficiencies associated with the conventional FMEA method, a lot of researches have been carried out in the past decades and various new risk priority models have been developed in the literature. For instance, Bowles and Peláez (1995) initially presented a fuzzy logic-based FMEA approach for dealing with some of the drawbacks in the traditional method of strictly numerical evaluation. Braglia et al. (2003b) proposed a f
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