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Failure mode and effect analysis (FMEA) is a systematic, multidisciplinary team-based risk management tool used in diverse industries to help improve the safety and reliability of systems, designs, processes and/or services. However, the traditional FMEA method, when applied in real situations, shows some important drawbacks regarding failure mode evaluations, risk factor weights and risk priority ranking, etc. In this paper, 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 is presented to illustrate the applicability and effectiveness of the proposed …