Developing a New Decision Support System to Manage Human Reliability based on HEART Method
الموضوعات :
Rasoul Jamshidi
1
(School of Engineering, Damghan University, Iran)
الکلمات المفتاحية: reliability, Artificial Neural Network, Decision support system, HEART, HEP,
ملخص المقالة :
Human performance and reliability monitoring have become the main issue for many industries since human error ratios cannot be mitigated to the zero level and many accidents, malfunctions, and quality defects are happening due to the human in production systems. Since the human resources implement a different range of tasks, the calculation of human error probability (HEP) is complicated, and several methods have been proposed to identify and quantify the HEP. This fact expresses the necessity of a Decision Support System (DSS) to calculate the HEP and propose optimal scenarios to increase human reliability and decrease its related cost such as quality defect and rework cost. This study develops a DSS that calculates the HEP based work specifications and proposes optimal scenarios to deal with error occurrence probability. The scenarios are provided using an AHP according to experts' opinions about the cost and time of corrective actions. The proposed DSS has been applied to a real case, and the provided results show that the proposed DSS can provide effective scenarios to deal with human error in production systems.
Akyuz, E., & Celik, M. (2015). Computer-Based Human Reliability Analysis Onboard Ships. Procedia - Social and Behavioral Sciences, 195(Supplement C), 1823-1832.
Boring, R. (2007). Dynamic human reliability analysis: Benefits and challenges of simulating human performance. Risk Reliab and Society Safety, 2, 1043-1049.
Bumblauskas, D., Gemmill, D., Igou, A., & Anzengruber, J. (2017). Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics. Expert systems with applications, 90(Supplement C), 303-317.
Calhoun, J., Savoie, C., Randolph-Gips, M., & Bozkurt, I. (2013). Human Reliability Analysis in Spaceflight Applications. Quality and Reliability Engineering International, 29(6), 869-882.
Chang, Y. H. J., & Mosleh, A. (2007a). Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents. Part 2: IDAC performance influencing factors model. Reliability Engineering & System Safety, 92(8), 1014-1040.
Chang, Y. H. J., & Mosleh, A. (2007b). Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents. Part 4: IDAC causal model of operator problem-solving response. Reliability Engineering & System Safety, 92(8), 1061-1075.
Chang, Y. H. J., & Mosleh, A. (2007c). Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents: Part 1: Overview of the IDAC Model. Reliability Engineering & System Safety, 92(8), 997-1013.
Chang, Y. H. J., & Mosleh, A. (2007d). Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents: Part 3: IDAC operator response model. Reliability Engineering & System Safety, 92(8), 1041-1060.
Chang, Y. H. J., & Mosleh, A. (2007e). Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents: Part 5: Dynamic probabilistic simulation of the IDAC model. Reliability Engineering & System Safety, 92(8), 1076-1101.
Cooper, S., Ramey-Smith, A., Wreathall, J., Parry, G., Bley, D., Luckas, W., & Taylor, J. (1996). A Technique for Human Error Analysis (ATHEANA) -Technical Basis and Metodology. Washington DC.
Di Pasquale, V., Iannone, R., Miranda, S., & Riemma, S. (2013). An Overview of Human Reliability Analysis Techniques in Manufacturing Operations. In: Massimiliano Schiraldi (Ed.). 221-240.
Di Pasquale, V., Miranda, S., Iannone, R., & Riemma, S. (2015). A Simulator for Human Error Probability Analysis (SHERPA). Reliability Engineering & System Safety, 139, 17-32.
Embrey, D. E., Humphreys, P. C., Rosa, E. A., Kirwan, B., & Rea, K. (1984). SLIM-MAUD: An approach to assessing human error probabilities using structured expert judgement. Washington DC.
Gertman, D., Blackman, H., Marble, J., Byers, J., & Smith, C. (2005). The SPAR-H human reliability analysis method. Washington.
Graupe, D. (2007). Principles of Artificial Neural Networks (second ed.). USA: WSPC, USA.
Guo, H., Wang, W., Guo, W., Jiang, X., & Bubb, H. (2012). Reliability analysis of pedestrian safety crossing in urban traffic environment. Safety Science, 50(4), 968-973.
Hollnagel, E. (1998a). Cognitive reliability and error analysis method. Amsterdam: Elsevier.
Hollnagel, E. (1998b). Cognitive reliability and error analysis method (CREAM). Amsterdam: Elsevier.
LaSala, K. P., Roush, M. L., & Matic, Z. (1995). A Decision-Support Approach for the Design of Human-Machine Systems and Processes. Paper presented at the Annual Reliability and Maintainability Symposium Proceedings.
Le, Y., Qiang, S., & Liangfa, S. (2012). A novel method of analyzing quality defects due to human errors in engine assembly line. IEEE, 154-157.
Passino, K. M. (2005). Biomimicry for optimization, control and automation, . London: Springer-Verlag.
Pyy, P. (2000). An approach for assessing human decision reliability. Reliability Engineering & System Safety, 68(1), 17-28.
Reason, J. H. (2006). Human factors: a personal perspective. Paper presented at the Human Factors Seminar, Helsinki, Finland.
