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

Journal of Industrial Engineering International

دوره 19، شماره 1، خرداد 2023، صفحه 13-31   

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.30495/jiei.2023.1956520.1220

نویسندگان

salima ZEGHDANI^* ؛ Kinza Nadia MOUSS

Department of Industrial Engineering, Batna2 University, Batna, Algeria

چکیده

A significant issue for businesses is the risks connected to the production system, which can be related to people and their expertise, the technology being utilized, or the environment, endangering their productivity, continuity, and development. Risk assessment procedures are necessary to determine the most likely source of an undetected hazardous event that prevents the system from performing its function. This paper proposes a probabilistic methodology to evaluate the risks associated with a combed spinning workshop factory inside a textile company. It is a hybrid strategy based on the BN and the MADS-MOSAR methodology. In this proposal, we employ the MADS model to pinpoint risk sources from both human and environmental sources. The MOSAR approach, however, has some quantitative drawbacks. To get past this issue, we also present the integration of BN. The results of the experiments show that the suggested technique is competitive and more effective for managing and reducing risks; all types of enterprises can use it.

کلیدواژه‌ها

Risks management؛ MOSAR method؛ MAD model؛ Bayesian Network؛ Fault Tree

مراجع

[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.