TY - JOUR ID - 133616 TI - Neural network based human reliability analysis method in production systems JO - Journal of Applied Research on Industrial Engineering JA - JARIE LA - en SN - 2538-5100 AU - Jamshidi, Rasoul AU - Sadeghi, Mohammad Ebrahim AD - Department Industrial of Engineering, School of Engineering, Damghan University, Damghan, Iran. AD - Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran. Y1 - 2021 PY - 2021 VL - 8 IS - 3 SP - 236 EP - 250 KW - Human reliability analysis KW - Error Prediction KW - Performance shaping factors KW - cognitive factors DO - 10.22105/jarie.2021.277071.1274 N2 - Nowadays, many accidents, malfunctions, and quality defects are happening in production systems due to Human Errors Probability (HEP). Human Reliability Analysis (HRA) methods have been proposed to measure the HEP based on Performance Shaping Factors (PSFs), but these methods do not have a procedure to select the effective PSFs and consider the PSFs dependency. In this paper, we propose an Artificial Neural Network based Human Reliability Analysis (ANNHRA) in cooperation with Response Surface Method (RSM). This framework uses the advantage Systematic Human Error Reduction and Prediction Approach (SHERPA) method to quantify the PSFs and the ANN and RSM to consider the PSFs dependency and select the most effective PSFs. This framework decreases the time and cost and increases the accuracy of HRA. The proposed framework has been applied to a real case and the provided results show that human reliability can be calculated more effectively using ANNHRA framework. UR - https://www.journal-aprie.com/article_133616.html L1 - https://www.journal-aprie.com/article_133616_0da9f69b353714322085ac9ea4ac39e9.pdf ER -