A probabilistic hybrid methodology for the management and control of risks related to the production system: Case of industrial textiles

Subject Areas : Multivariate Analysis

salima ZEGHDANI ¹ , Kinza MOUSS ²

1 - Department of Industrial Engineering, Batna2 University, Batna, Algeria
2 - Department of Industrial Engineering, Batna2 University, Batna, Algeria

Received: 2022-04-12 Accepted : 2023-09-11 Published : 2023-03-01

Keywords:

References:

[1] Heutmann T., Tils A W & Schmitt R H., (2020). ‘’Quantifying disturbance risks on the process time for a robust, synchronized individual production’’. Production Engineering 14:289–296, https://doi.org/10.1007/s11740-020-00956-x

[2] Zerrouki H & Smadi H, (2019), ‘’Reliability and safety analysis using fault tree and Bayesian network’’. Int. J. of Computer Aided Engineering and Technology, Vol. 11,No.1.

[3] Tixier J., Dusserre G., Salvi O & Gaston D., (2002), ‘’Review of 62 risks analysis methodologies of industrial plants’’, Journal of Loss Prevention in the Process Industries, 15: 291–303.

[4] Bultel Y., Aurousseau M., Ozil P & Perrin L., (2007). ‘’Risk analysis on a fuel cell in electric vehicle using the MADS/MOSAR methodology’’, Process Safety and Environmental Protection, 85(3), 241-250.

[5] Gallab M., Bouloiz H & Tkiouat M., (2017) ‘Towards a model for developing an information system as a decision support to risk assessment’, Int. J. Industrial and Systems Engineering, Vol. 25, No. 1, pp.110–129.

[6] Hamzaoui F., Allal M A., Taillandier F & Achoui M., (2019). ‘’Risk management in construction projects by coupling the SMACC agent with the MADS MOSAR method – application to the dam project in Mascara, Algeria’’. Int. J. of Construction Management, 2019, ISSN: 1562-3599 (Print) 2331-2327 (Online)

[7] Smaiah M., Djebabra M & Bahmed L., (2017). ‘’Contribution to the Improvement of the MADS–MOSAR Method for the Modeling of Domino Effects’’. Journal of Failure Analysis and Prevention. 17:440–449.
[8] Zeghdani S., (2015) "Modélisation de l’état d’un système de production sur la base d’une approche Bayésienne. Etude de cas : Entreprise COTITEX – BATNA". Mémoire de magistère. Département génie industriel, Laboratoire d’Automatique et Productique (LAP). Université Batna 2, Algérie.
[9] Bobbio A., Portinale L., Minichino M & Ciancamerla E., (2001). ‘’Improving the analysis of dependable systems by mapping FTs into Bayesian networks’’. Journal of Reliability Engineering & System Safety 2001;71:249–60.
[10] Jones B., Jenkinson I., Yang Z & Wang J., (2010). ‘’The use of Bayesian network modelling for maintenance planning in a manufacturing industry’’. Reliability Engineering & System Safety 95 (2010) 267–277
[11] Khakzad N., Khan F & Amyotte P., (2011). "Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches," Reliability Engineering & System Safety, vol. 96, No. 8, pp. 925-932.
[12] Zheng Y, Zhao F & Wang Z, (2019), ‘’Fault diagnosis system of bridge crane equipment based on fault tree and Bayesian network’’. Int. J. of Advanced Manufacturing Technology (2019) 105:3605–3618. https://doi.org/10.1007 /s00170-019-03793-0
[13] Haugom G P & Friis-Hansen P., (2011). ‘’Risk modelling of a hydrogen refuelling station using Bayesian network’’. Int. J. of Hydrogen Energy 36 (3), 2389-2397.
[14] Cao M, Zheng P., Liu D., Chang J & Zhang L., (2021). “In‑process Measurement and Geometric Error Fusion Control of Discontinuous Surface Based on Bayesian Theory”. International Journal of Precision Engineering and Manufacturing. 22:539–556; https://doi.org/10.1007/s12541-021-00493-2
[15] Liu J., Li Y., Ma X., Wang L & Li J., (2021). ‘’ Fault Tree Analysis Using Bayesian Optimization: A Reliable and Effective Fault Diagnosis Approaches‘’. Journal of Failure Analysis and Prevention. (2021) 21:619–630; https://doi.org/10.1007/s11668-020-01096-1
[16] Smith D., Veitch B., Khan F & Taylor R., (2016), “Understanding industrial safety: Comparing fault tree, Bayesian network, and FRAM approaches”, Journal of Loss Prevention in the Process Industries, PII: S0950-4230(16)30426-0, DOI: 10.1016/j.jlp.2016.11.016
[17] Barua, S., Gao, X., Pasman, H & Mannan, M.S., (2016). ‘’Bayesian network based dynamic operational risk assessment’’. Journal of Loss Prevention in the Process Industries.41, 399-410.
[18] Huang Y., G Ma & J Li., (2017). ”Grid-based risk mapping for gas explosion accidents by using Bayesian network method”. Journal of Loss Prevention in the Process Industries 48 (2017) 223-232.
[19] Norazahar, N., Khan, F., Veitch, B & MacKinnon, S., (2017). Prioritizing safety critical human and organizational factors of EER systems of offshore installations in a harsh environment. Safety Science. 95, 171-181.
[20] Eickemeyer S C., Borcherding T., Scha¨fer S & Nyhuis P., (2013). ‘’Validation of data fusion as a method for forecasting the regeneration workload for complex capital goods’’. Prod Eng Res Devel. (2013) 7:131–139, DOI 10.1007/s11740-013-0444-8
[21] Amit Sata & B Ravi., (2017). “Bayesian inference-based investment-casting defect analysis system for industrial application”. Int. J. of Advanced Manufacturing Technology. 90:3301–3315. DOI 10.1007/s00170-016-9614-0
[22] Roth S., Kalchschmid V & Reinhart G., (2021) ‘’Development and evaluation of risk treatment paths within energy‑oriented production planning and control’’. Production Engineering (2021) 15:413–430 https://doi.org/10.1007/s11740-021-01043-5.
[23] Dahia Z, Bellaouar A and Dron J-P (2021). ‘’A dynamic approach for maintenance evaluation and optimization of multistate system’’. Journal of Industrial Engineering International, Volume 17, issue 1, march 2021, p 1 à 13. DOI 10.30495/JIEI.2021.1926554.1110
[24] Périlhon P., (2003). ‘’MOSAR présentation de la méthode’’, Techniques de l’ingénieur, 2003, fascicule SE 4 060.
[25] Weber P & Suhner M., (2004). ‘’Modélisation de processus industriels par Réseaux Bayésiens Orientés Objet (RBOO)’’.Revue d'Intelligence Artificielle 18 (2004) 299-326. Centre de Recherche en Automatique de Nancy (CRAN), France.
[26] Tohidi,H., Jabbari, M.M., (2012). ‘’ CRM in organizational structure design’’. Procedia Technology, 1(2012), 579-582.
https://doi.org/10.1016/j.protcy.2012.02.126
[27] Tohidi,H., Jabbari, M.M., (2012). ‘’ The necessity of using CRM ’’. Procedia Technology, 1(2012), 514-516. https://doi.org/10.1016/j.protcy.2012.02.110
[28] Naim P., Henri P., Wuillemin K., Leray P., Pourret O & Becker A., (2007). ‘’Réseaux Bayésiens’’, Livre, 3ème Edition, Eyrolles.

[29] I Yusuf and A Sanus (2023). ‘’Copula Approach for Reliability and Performance Estimation of Manufacturing System’’. Journal of Industrial Engineering International, 18(2), June 2022.
[30] K Khalili-Damghani, M Poortarigh, A Pakgohar (2017). ‘’A new model for probabilistic multi-period multi-objective project selection problem’’, 24th International Conference on Production Research (ICPR 2017), 598-603.
[31] K Khalili-Damghani, A Shahrokh, A Pakgohar (2017). ‘’Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain’’. International Journal of Production Management and Engineering 5 (2), 85-106
[32] P Ghasemi, K Khalili-Damghani, A Hafezalkotob, S Raissi (2017). ‘’Stochastic optimization model for distribution and evacuation planning (A case study of Tehran earthquake)’’. Socio-Economic Planning Sciences 71, 100745.