Risk Management Hybrid Decision-Making Support Methodology in Complex Sociotechnical Systems

Abstract

The paper presents a hybrid method of risk analysis in the complex systems predicting the possible accident development associated with the social systems, as well as recommendations in prevention of such accidents. The proposed method in order to determine operational state of a complex system and endow it with additional ability to withstand failures combines system dynamics models (to help in identifying interactions of the elements of the system under study in dynamics), event and failure tree models (used to simulate the risk scenario evolution) and artificial neural networks. The hybrid risk management methodology in sociotechnical systems is based on combining capabilities of different artificial intelligence technologies and makes it possible to introduce advantages of several technologies by integrating them. Six stages of research carried out within the framework of hybrid technique are presented, as well as mathematical description of the neural network model. Effectiveness of the proposed methodology was tested using three implemented software products. On the example of a construction company and using the developed original software package, accident scenarios were simulated, and a neural net-work was built to predict risks and determine the company operation status. Simulation results are provided

The work was performed within the framework Priority 2030 Program,of Bauman Deep Analytics Project

Please cite this article in English as:

Kiwan M., Berezkin D.V., Smirnova E.V. Risk management hybrid decision-making support methodology in complex sociotechnical systems. Herald of the Bauman Moscow State Technical University, Series Instrument Engineering, 2023, no. 2 (143), pp. 90--110 (in Russ.). DOI: https://doi.org/10.18698/0236-3933-2023-2-90-110

References

[1] Kivan M., Berezkin D.V., Raad M., et al. Analyzing common approaches of accident models for risk management in socio-technical systems. Dinamika slozhnykh system --- XXI vek [Dynamics of Complex Systems --- XXI Century], 2021, no. 1, pp. 22--37 (in Russ.). DOI: https://doi.org/10.18127/j19997493-202101-03

[2] Kivan M., Berezkin D.V., Khamed A. Hybrid approaches of accident modeling for risk management in socio-technical systems. Dinamika slozhnykh system --- XXI vek [Dynamics of Complex Systems --- XXI Century], 2021, no. 2, pp. 14--27 (in Russ.).DOI: https://doi.org/10.18127/j19997493-202102-02

[3] Heinrich H.W. Industrial accident prevention. A scientific approach. New York, McGraw-Hill, 1931.

[4] Leveson N.G. Safeware. System safety and computers. Washington, Addison-Wesley, 1995.

[5] Hollnagel E. Barriers and accident prevention. Farnham, Ashgate Publishing, 2004.

[6] Rasmussen J. Risk management in a dynamic society: a modelling problem. Saf. Sc., 1997, vol. 27, no. 2-3, pp. 183--213. DOI: https://doi.org/10.1016/S0925-7535(97)00052-0

[7] Leveson N.G. A new accident model for engineering safer systems. Saf. Sc., 2004, vol. 42, no. 4, pp. 237--270. DOI: https://doi.org/10.1016/S0925-7535(03)00047-X

[8] Hollnagel E. Cognitive reliability and error analysis method (CREAM). New York, Elsevier, 1998.

[9] Hollnagel E., Goteman O. The functional resonance accident model. Proc. Cognitive System Engineering in Process Plant, 2004, pp. 155--161.

[10] Gao J., Tian J., Zhao T. An improved system safety Analysis Method based on Accimap. Proc. IEEE IEEM, 2015, pp. 1142--1146. DOI: https://doi.org/10.1109/IEEM.2015.7385827

[11] Clemens P.L. Event tree analysis. JE Jacobs Sverdrup, 2002.

[12] Newhall C., Hoblitt R. Constructing event trees for volcanic crises. Bull. Volcanol, 2002, vol. 64, no. 1, pp. 3--20. DOI: https://doi.org/10.1007/s004450100173

[13] Gharahasanlou A.N., Mokhtarei A., Khodayarei A., et al. Fault tree analysis of failure cause of crushing plant and mixing bed hall at Khoy cement factory in Iran. Case Stud. Eng. Fail. Anal., 2014, vol. 2, no. 1, pp. 33--38. DOI: https://doi.org/10.1016/j.csefa.2013.12.006

[14] Huang W., Liu Y., Zhang Y., et al. Fault Tree and Fuzzy DS Evidential Reasoning combined approach: an application in railway dangerous goods transportation system accident analysis. Inf. Sc., 2020, vol. 520, pp. 117--129. DOI: https://doi.org/10.1016/j.ins.2019.12.089

[15] Winch G. Dynamic visioning for dynamic environments. J. Oper. Res. Soc., 1999, vol. 50, no. 4, pp. 354--361. DOI: https://doi.org/10.1057/palgrave.jors.2600648

[16] Morecroft J.D.W. Management attitudes, learning and scale in successful diversification: a dynamic and behavioural resource system view. In: System dynamics. Cham, Springer-Nature, 2018, pp. 69--106. DOI: https://doi.org/10.1057/palgrave.jors.2600648

[17] Chitsazan N., Nadiri A.A., Tsai F.T.-C. Prediction and structural uncertainty analyses of artificial neural networks using hierarchical Bayesian model averaging. J. Hydrol., 2015, vol. 528, pp. 52--62. DOI: https://doi.org/10.1016/j.jhydrol.2015.06.007

[18] Ashtiani H.R.R., Shahsavari P. A comparative study on the phenomenological and artificial neural network models to predict hot deformation behavior of AlCuMgPb alloy. J. Alloys Compd., 2016, vol. 687, pp. 263--273. DOI: https://doi.org/10.1016/j.jallcom.2016.04.300

[19] Baybutt P. A critique of the Hazard and Operability (HAZOP) study. J. Loss Prev. Process Ind., 2015, vol. 33, pp. 52--58. DOI: https://doi.org/10.1016/j.jlp.2014.11.010

[20] Sterman J. Business dynamics. New York, McGraw-Hill, 2000.

[21] Aburawi I., Hafeez K. Managing dynamics of human resource and knowledge management in organizations through system dynamics modelling. Int. J. Sc. Tech. Autom. Control Eng., 2009, vol. 3, no. 2, pp. 1108--1125.

[22] Ciaburro G., Venkateswaran B. Neural networks with R. Mumbai, Packt Publ., 2017.

[23] PyTorch. pytorch.org: website. Available at: https://pytorch.org (accessed: 03.02.2023).

[24] NumPy. numpy.org: website. Available at: https://numpy.org (accessed: 03.02.2023).

[25] Machine learning in Python. scikit-learn.org: website. Available at: http://scikit-learn.org/stable/index.html (accessed: 03.02.2023).

[26] The Sequential class. keras.io: website. Available at: https://keras.io/models/sequential (accessed: 03.02.2023).

[27] TensorFlow. tensorflow.org website. Available at: http://tensorflow.org (accessed: 03.02.2023).

[28] Probabilistic risk analysis tool. github.com: website. Available at: https://github.com/rakhimov/scram/tree/gh-source (accessed: 03.02.2023).

[29] Shampine L.F. Numerical solution of ordinary differential equations. Milton Park, Abingdon, Routledge, 2018.