Risk assessment in discrete production processes considering uncertainty and reliability: Z-number multi-stage fuzzy cognitive map with fuzzy learning algorithm

The Failure Mode and Effects Analysis (FMEA) technique due to its proactive nature can identify failures and their causes as well as potential effects, and provide preventive/controlling measures before they occur. Nevertheless, some of the shortcomings of the FMEA technique like lack of a mental framework for considering the relationships between risks, lack of systematic perspective in confronting with risks, and weakness of Risk Priority Number (RPN) score in mathematical basis and disregarding the uncertainty of problem reduce the reliability of the outputs. In this study, an approach based on the Multi-Stage Fuzzy Cognitive Map and the Z-number theory (Z-MSFCM) is proposed to simultaneously consider the concept of uncertainty and reliability in quantities of risk factors and the weights of causal relationships in the MSFCM. Besides, a novel learning approach for Z-MSFCM has been applied based on the combination of the Particle Swarm Optimization (PSO) and S-shaped transfer function (PSO-STF) to preserve the uncertain environment of the problem. The proposed approach has been applied in a manufacturing automotive parts company and results indicate that: first, Z-MSFCM by considering the causal relationships between risks and their uncertainty and reliability in comparison with traditional RPN can provide better process-oriented insight into the impact of risks on the system; and second, the PSO-STF has high potential in generating solutions with high separability compared to Nonlinear Hebbian Learning and PSO algorithms. To put it differently, the mentioned advantages of the proposed approach can help decision-makers to analyze the problem with high reliability.

Automated summary

This study proposes an approach based on the Multi-Stage Fuzzy Cognitive Map and the Z-number theory to simultaneously consider uncertainty and reliability in quantities of risk factors and causal relationships. By applying the PSO-STF learning approach, high potential solutions with high separability can be generated in uncertain environments, aiding decision-makers in analyzing problems with high reliability.