@revista_internacional{946,
  keywords = {Failure mode and effect analysis, Dempster–Shafer theory, Stochastic multi-objective acceptability analysis, Best-worst method, Measurement of alternatives and ranking according to compromise solution},
  author = {Yanbing Ju and Qian Zhao and Luis Martínez and Yuanyuan Liang and Jinhua Dong and Peiwu Dong and Mihalis Giannakis},
  title = {A novel framework for FMEA using evidential BWM and SMAA-MARCOS method},
  abstract = {This paper presents a novel failure mode and effect analysis (FMEA) framework as a formal design method to ensure safety and reliability. FMEA is used to identify potential failure modes (FMs), and it is crucial to determine the weights of risk factors and prioritize FMs. In this work, we propose a comprehensive framework that integrates the Dempster-Shafer theory, best-worst method (BWM), stochastic multi-objective acceptability analysis (SMAA), and measurement of alternatives and ranking according to compromise solution (MARCOS) to address this problem. To capture the uncertainty caused by the loss of information, the Dempster-Shafer theory is applied for dealing with the uncertainty about risk factors and FMs in terms of linguistic information. Based on the comprehensive evidential preference interval vectors of risk factors constructed by Dempster-Shafer theory, an evidential BWM combined with SMAA is proposed to determine the optimal set of risk factor weights. Meanwhile, based on the constructed interval belief interval decision matrix of FMs to risk factors constructed by Dempster-Shafer theory, an evidential SMAA-MARCOS method is proposed for determining the risk priority of FMs. Further, we conduct a case study to evaluate the risk of equipment in an automobile manufacturing enterprise. A sensitivity and comparative analysis are also conducted to demonstrate the effectiveness and superiority of the proposed framework.},
  year = {2024},
  journal = {Expert Systems with Applications},
  volume = {243},
  pages = {122796},
  issn = {0957-4174},
  url = {https://www.sciencedirect.com/science/article/pii/S0957417423032980},
  doi = {https://doi.org/10.1016/j.eswa.2023.122796},
}