Title: Risk management framework based on multi-criteria decision method and artificial intelligence tools

Authors: Adil Waguaf; Rajaa Benabbou; Jamal Benhra

Addresses: Optimisation of Logistic and Industrial Team, Advanced Research Laboratory in Industrial Engineering and Logistics 'LARILE', National Higher School of Electricity and Mechanics, Hassan II University of Casablanca, Casablanca, Morocco ' Optimisation of Logistic and Industrial Team, Advanced Research Laboratory in Industrial Engineering and Logistics 'LARILE', National Higher School of Electricity and Mechanics, Hassan II University of Casablanca, Casablanca, Morocco ' Optimisation of Logistic and Industrial Team, Advanced Research Laboratory in Industrial Engineering and Logistics 'LARILE', National Higher School of Electricity and Mechanics, Hassan II University of Casablanca, Casablanca, Morocco

Abstract: Companies invest in the implementation of risk management frameworks that are efficient, structured, and in accordance with the ISO 31000 standard. Our work is to propose a new global methodology for developing a risk management framework based on artificial intelligence tools: genetic algorithms GA for calibrating weighting coefficients and calculating weights; the MCDM multi-criteria decision-making method, especially the 'technical for order of priority by similarity to ideal solution' 'TOPSIS' method for the evaluation and acceptability of risks; and artificial neural networks for the prediction of the cost of treatment of risks and the number of work accidents from historical data of work accidents. This framework will allow the automation of the process to facilitate acquisition and its objectivity. The results obtained are satisfactory based on the calculation of the error.

Keywords: ISO 31000; artificial neural networks; risk management framework; genetic algorithm; TOPSIS.

DOI: 10.1504/IJBCRM.2023.134486

International Journal of Business Continuity and Risk Management, 2023 Vol.13 No.4, pp.364 - 381

Received: 19 Nov 2022
Accepted: 19 Feb 2023
Published online: 24 Oct 2023 *

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