TALHA*DEVELOPİNG A MACHİNE BASED DYNAMİC RİSK ANALYSİS DECİSİON SUPPORT SYSTEM

Yıl 2020, Prof. Dr. Talha Ustasüleyman Özel Sayısı, 149 - 166, 20.02.2020

Selçuk Çebi , Hakan Temizoğlu

https://doi.org/10.18092/ulikidince.579073

Cited By: 2

Öz

Risk
assessment, which is both a legal and a conscientious requirement for every
business, consists of risk analysis and risk control phases and it is a process
that must be implemented for each work environment. Risk analysis is the phase
that includes hazard identification and risk estimation while risk evaluation
is the phase that involves planning judgments on protective measures for risk
reduction and observing whether the risk reduction objectives have been
achieved. Each step of the risk assessment requires expertise knowledge and
perspective. The Regulation on Risk
Assessment of Occupational Health and Safety (Official Gazette No. 28512,
December 29, 2012) made both time-based and case-based definitions for the
renewal of risk assessment studies. However, dependency of related or
interrelated activities on human factor causes safety vulnerability because of
human errors. Therefore, it is required to develop a dynamic risk assessment by
increasing contribution of employee and proposing dynamic protective measures
for safety vulnerabilities in the system. 
In another words, it is required to manage risk assessment process in a
dynamic structure. For this, business should set and utilize dynamic risk
assessment decision support system. The main objective of this study is to
present an algorithm for dynamic risk assessment decision support system.
Hence, risk analysis in the work environment can be done simultaneously and
protective measures can be planned and implemented at the same time. This
structure will provide a basic for Industry 4.0 in terms of occupational safety
and the system developed by integrating sensors and actuators provide
occupational safety at work environment by minimizing requirements to human
expertise and communicating with work environment continuously.

Anahtar Kelimeler

Risk assessment, Risk analysis, Dynamic risk analysis, Decision support system, Industry 4.0

Proje Numarası

7160897

MAKİNE TABANLI DİNAMİK RİSK ANALİZİ İÇİN BİR KARAR DESTEK SİSTEMİ GE-LİŞTİRME

Öz

Her işletme için hem kanuni hem de vicdani gereklilik olan risk
değerlendirmesi, risk analizi ve risk kontrol aşamalarının bir birleşimi olarak
her çalışma ortamında uygulanmak zorunda olan bir süreçtir.   Risk analizi, tehlikelerin tanımlandığı ve
risk büyüklüğünün tahmin edildiği aşama iken risk kontrolü, risk azaltma için
tedbirlerin ya da önlemlerin planlandığı ve hedefe ulaşma düzeyinin izlendiği
aşamadır. Risk değerlendirme sürecinin her aşaması uzman bilgisi ve bakış açısı
gerektirir ve risklerin yönetimi açısından her adım eş öneme sahiptir. İş Sağlığı ve Güvenliği Risk Değerlendirmesi
Yönetmeliği (29.12.2012 Resmi Gazete Sayısı: 28512), risk değerlendirme
çalışmalarının yenilenmesi için hem zaman bazlı hem de durum bazlı tanımlamalar
yapmıştır. Ancak işletme içerisinde birbiriyle ilişkili ve iç içe yürütülen
faaliyetlerin büyük bir kısmının günümüzde insana bağlı olarak yürütülmesi
üretim süreçlerinde insan hatasına bağlı olarak iş güvenliği önlemlerinde açık
kapı oluşturmaktadır. Bu nedenle risk değerlendirme çalışmalarına çalışan
katılımı arttırılarak risk analizlerin dinamik olarak yenilenmesi ve sistemdeki
güvenlik açıklarına karşı anlık önlemlerin önerilmesi gerekir. Diğer bir
ifadeyle, risk değerlendirme sürecinin dinamik bir yapıda yönetilmesi gerekir.
Bunun için işletmelerde dinamik risk analizi karar destek sistemlerinin
kuruluyor ve işletiliyor olması gerekir. Bu çalışmanın ana amacı işletmelerde
kurulacak dinamik risk analizi karar destek sistemi için bir algoritma
önermektir. Böylece işletme içerisindeki risk analizleri anlık olarak
yenilenebilecek ve güvenlik açıklarına karşı anında eylemler
planlanabilmektedir. Bu yapı Endüstri 4.0 için nitelikli bir alt yapı
oluşturacak ve ileride sisteme entegre edilecek algılayıcılar ve eyleyiciler
yardımıyla geliştirilecek sistem, uzman müdahalesi minimuma indirerek ve
çalışma ortamıyla sürekli haberleşerek çalışma ortamının iş güvenliğini sürekli
olarak sağlamış olacak.

Anahtar Kelimeler

Risk değerlendirme, Risk analizi, Dinamik risk analizi, Karar destek sistemi, Endüstri 4.0

Destekleyen Kurum

TÜBİTAK 1507 KOBİ AR-GE Başlangıç Destek Programı

Proje Numarası

7160897

Teşekkür

Bu çalışma TÜBİTAK 1507 KOBİ AR-GE Başlangıç Destek Programı tarafından desteklenen 7160897 numaralı projeden çıkmış olup yazarlar desteklerinden dolayı TÜBİTAK’a teşekkürü bir borç bilirler.

Kaynakça

• Abdelgawad, M. Fayek, A. R. (2010). Risk management in the construction industry using combined fuzzy FMEA and fuzzy AHP, Journal of Construction Engineering and Management, 136 (9) 1028-1036.
• Acuner, Ö., Çebi, S. (2016). An Effective Risk-Preventive Model Proposal for Occupational Accidents at Shipyards, Brodogradnja/Shipbuilding, 67(1), 67-84.
• Buckley, J.J., (1985). Ranking alternatives using fuzzy numbers, Fuzzy Sets Systems, 15 (1), 21-31.
• Chen, S., J. ve Hwang, C., L. (1992). Fuzzy Multi Attribute Decision Making: Methods and Applications, Lecture Notes in Economics and Mathematical Systems, Springer-Verlag, New York.
• Çebi, S., Akyuz, E., Şahin, Y. (2017), Developing Web Based Decision Support System For Evaluatıon Occupational Risks At Shipyards, Brodogradnja/Shipbilding, 68 (1).
• Çebi, S., İlbahar, E., (2018a). Warehouse Risk Assessment Using Interval Valued Intuitionistic Fuzzy AHP, International Journal of the Analytic Hierarchy Process, 10 (2),
• Çebi, S., İlbahar, E., (2018b). Tersanelerde Yaşanan Mesleki Risklerin Analizi İçin Bulanık Papyon Model Önerisi, Journal of ETA Maritime Science, 6(2): 141-157
• Durga Rao, K., Gopika, V., Sanyasi Rao, V.V.S., Kushwaha, H.S., Verma, A.K., Srividya, A. (2009). Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment, Reliability Engineering & System Safety, 94, 872–883.
• Topuz, E. van Gestel, C. A. (2016) An approach for environmental risk assessment of engineered nanomaterials using Analytical Hierarchy Process (AHP) and fuzzy inference rules, Environment international, 501, 334-347.
• Gul, M., Ak, M. F., Guneri, A. F. (2019). Pythagorean fuzzy VIKOR-based approach for safety risk assessment in mine industry, Journal of Safety Research, 69, 135-153.
• Hsieh, T., Y., Lu, S., T. and Tzeng, G., T. (2004). Fuzzy MCDM Approach for Planning and Design Tenders Selection in Public Office Buildings, International Journal of Project Management, 22, 573–584.
• İlbahar E., Karaşan A., Çebi S., Kahraman C., (2018). A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system, Safety Science, 103, 124-136.
• Karaşan A., İlbahar E., Çebi S., Kahraman C., (2018). A new risk assessment approach: Safety and Critical Effect Analysis (SCEA) and its extension with Pythagorean fuzzy sets, Safety Science,108, 173-187.
• Khakzad, N., 2015. Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures, Reliability Engineering & System Safety, 138, 263–272.
• Khakzad, N., Khan, F., Amyotte, P., 2013a. Risk-based design of process systems using discrete-time Bayesian networks, Reliability Engineering & System Safety, 109, 5–17.
• Khakzad, N., Khan, F., Paltrinieri, N. (2014). On the application of near accident data to risk analysis of major accidents, Reliability Engineering & System Safety, 126.
• Mamdani, E. H. (1977). Application of fuzzy logic to approximate reasoning using linguistic synthesis, IEEE Transactions on Computers, 26(12): 1182–1191.
• Nieto-Morote, A. Ruz-Vila, F. (2011). A fuzzy approach to construction project risk assessment, International Journal of Project Management, 29 (2), 220-231.
• Nivolianitou, Z.S., Leopoulos, V.N., Konstantinidou, M. (2004). Comparison of techniques for accident scenario analysis in hazardous systems, Journal of Loss Prevention in the Process Industries, 17,467–475.
• Noh, Y., Chang, K., Seo, Y., Chang, D. (2014). Risk-based determination of design pressure of LNG fuel storage tanks based on dynamic process simulation combined with Monte Carlo method, Reliability Engineering & System Safety, 129, 76–82.
• Nývlt, O., Haugen, S., Ferkl, L., (2015). Complex accident scenarios modelled and analysed by Stochastic Petri Nets, Reliability Engineering & System Safety, 142, 539–555.
• Nývlt, O., Rausand, M.ö(2012). Dependencies in event trees analyzed by Petri nets, Reliability Engineering & System Safety, 104, 45–57.
• Paltrinieri, N., Comfort, L., Reniers, G. (2019). Learning about risk: Machine learning for risk assessment, Safety Science, 118, 475–486
• Paltrinieri, N., Khan, F., Amyotte, P., Cozzani, V. (2014). Dynamic approach to risk management: application to the Hoeganaes metal dust accidents, Process Safety and Environmental Protection,92.
• Paltrinieri, N., Khan, F., Cozzani, V. (2015). Coupling of advanced techniques for dynamic risk management. J. Risk Res., 18, 910–930.
• Paltrinieri, N., Reniers, G. (2017). Dynamic risk analysis for Seveso sites, Journal of Loss Prevention in the Process Industries, 49.
• Rodriguez, A. Ortega, F. Concepcion, R. (2016). A method for the evaluation of risk in IT projects, Expert Systems with Applications, 45 273-285.
• Ross, T., J., (2004). Fuzzy Logic with Engineering Applications (3rd Ed.), John Wiley & Sons, Ltd, USA.
• Sarbayev, M., Yang, M., Wang, H., (2019), Risk assessment of process systems by mapping fault tree into artificial neural network, Journal of Loss Prevention in the Process Industries, 60, 203-212.
• Targoutzidis, A., (2012). A Monte Carlo simulation for the assessment of Bayesian updating in dynamic systems. Reliability Engineering & System Safety, 100, 125–132.
• Yang, M. Khan, F. I. Sadiq, R. (2011). Prioritization of environmental issues in o shore oil and gas operations: A hybrid approach using fuzzy inference system and fuzzy analytic hierarchy process, Process Safety and Environmental Protection, 89 (1), 22-34.
• Zeng, J. An, M. Smith, N.J. (2007). Application of a Fuzzy Basen Decision Making Methodology to Construction Project Risk Assessment, International Journal of Project Management, 25, 589–600.
• Zhou, J., Reniers, G. (2016a). Petri-net based simulation analysis for emergency response to multiple simultaneous large-scale fires. Journal of Loss Prevention in the Process Industries, 40, 554–562.
• Zhou, J., Reniers, G. (2016b). Petri-net based modeling and queuing analysis for resourceoriented cooperation of emergency response actions, Process Safety and Environmental Protection, 102,567–576.
• Zhou, J., Reniers, G. (2017). Petri-net based cascading effect analysis of vapor cloud explosions. Journal of Loss Prevention in the Process Industries, 48, 118–125.
• Zhou, J., Reniers, G., Zhang, L. (2017). A weighted fuzzy Petri-net based approach for security risk assessment in the chemical industry, Chemical Engineering Science174, 136–145.