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Vojnotehnički glasnik
2019, vol. 67, br. 3, str. 614-641
jezik rada: engleski
vrsta rada: pregledni članak
objavljeno: 12/07/2019
doi: 10.5937/vojtehg67-20900
Creative Commons License 4.0
Metodologija za procenu rizika - primena bajesovih mreža verovatnoće u projektu delaboracije municije
aMinistry of Defence of the Republic of Serbia, Sector for Material Resources, Belgrade
bUniverzitet u Kragujevcu, Fakultet inženjerskih nauka

e-adresa: slobodan.malbasic@mod.gov.rs, sdjuric@kg.ac.rs

Sažetak

Modeli koji reprezentuju realne probleme prilikom donošenja zaključaka većinom se oslanjaju na istorijske podatke. Negativan aspekt ovih modela jeste da oni ne mogu da predvide buduća stanja zasnovana na trenutno prikupljenim podacima kao i novim izvorima rizika. Da bi se prevazišao ovaj problem, u radu je prikazan proces izgradnje realnog prediktivnog modela korišćenjem Bajesovih mreža verovatnoće i softvera AgenaRisk. Bajesove mreže verovatnoće najdirektnije reprezentuju realne probleme preko grafičke strukture koja predstavlja uslovne veze, a ne samo tokove informacija. Razvijeni su i softveri koji imaju algoritme za računanje uslovnih verovatnoća. Kao teoretska osnova koristi se Bajesova teorema koja je takođe objašnjena u ovom radu. Druga prednost korišćenja Bajesovih mreža verovatnoće jeste proces zaključivanja koji se može vršiti u "oba pravca" (odozgo nadole i obratno), što ga čini veoma moćnim alatom u proceni rizika i procesu zaključivanja. Takođe, u radu su prikazani osnovni principi i prednosti primene Bajesovih mreža u procesu pripreme projekta delaboracije municije (rešavanje viškova i neperspektivne municije u skladištima). U njemu je procena rizika jedan od zahtevanih aktivnosti koji pomaže u procesu donošenja konačne odluke za pokretanje ili nepokretanje projekta. Analiza osetljivosti i SWOT analiza primenjeni su kao korisni alati za validaciju i donošenje konačnih zaključaka.

Ključne reči

Reference

Andrejić, M.D., Đorović, B.D., Pamučar, D.D. (2011) Managing projects using a project management approach. Vojnotehnički glasnik, vol. 59, br. 2, str. 142-157
Constantinou, A.C., Fenton, N., Marsh, W., Radlinski, L. (2004) From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support. Artificial Intelligence in Medicine, 67, pp.75-93
Fan, C.F., Yu, Y.C. (2004) BBN-based software project risk management. Journal of System and Software, 73(2), pp.193-203
Fang, C., Marle, F. (2012) A simulation-based risk network model for decision support in project risk management. Decision Support Systems, 52(3), pp.635-644
Fenton, N., Neil, M. (2011) The use of Bayes and causal modeling in decision makin, uncertainity and risk. https://pdfs.semanticscholar.org/92dc/7cf5f483f5ebe9a0fffc5afe6e87bc5627e5.pdf, 20.04.2018
Fenton, N., Neil, M. (2013) Risk assessment and decision analysis with Bayesian network. Boca Raton: CRC Press
Gadeberg, M., Luedeling, E. Can we build a better project: assessing complexities in development projects. https://wle.cgiar.org/thrive/2016/06/01/can-we-build-better-project-assessingcomplexities-development-projects, 10.09.2016
John, A., Yang, Z., Riahi, R., Wang, J. (2016) A risk assessment approach to improve the resilience of a seaport system using Bayesian networks. Ocean Engineering, 111, pp.136-147
Landuyt, D., Broekx, S., Dhondt, R., Engelen, G., Aertsens, J., Goethals, P.L.M. (2013) A review of Bayesian belief networks in ecosystem service modeling. Environmental Modelling & Software, 46, pp.1-11
Lee, E., Park, Y., Shin, J.G. (2009) Large engineering project risk management using a Bayesian belief network. Expert Systems with Applications, 36(3-Part2), pp.5880-5887
Malbašić, S., Tančić, L., Petrović, V. (2016) Technology risk assessment as part of risk management process. Serbian Project Management Journal, 6(1), 51-62
Marcelino-Sádaba, S., Pérez-Ezcurdia, A., Echeverría, L.A.M., Villanueva, P. (2014) Project risk management methodology for small firms. International Journal of Project Management, 32(2), pp.327-340
Marcot, B.G., Penman, T.D. (2019) Advances in Bayesian network modeling: Integration of modeling technologies. Environemntal Modeling & Software, 111, pp.386-393
Raz, T., Michael, E. (2001) Use and benefits of tools for project risk management. International Journal of Project Management, 19(1), pp.9-17
Starr, C., Shi, P. (2004) An introduction to Bayesian Belief Networks and their applications to land operations. https://www.researchgate.net/publication/267240702_An_Introduction_to_Bayesian, 15.09.2016
Tang, A., Nicholson, A., Jin, Y., Han, J. (2007) Using Bayesian belief networks for change impact analysis in architecture design. Journal of Systems and Software, 80(1), pp.127-148
Tien, I., Der, K.A. (2016) Algorithms for Bayesian network modeling and reliability assessment of infrastructure systems. Reliability Engineering & System Safety, 156, pp.134-147
Weber, P., Medina-Oliva, G., Simon, C., Iung, B. (2012) Overview on 41 Bayesian networks application for dependability, risk analysis and maintenance. Engineering Applications of Artificial Intelligence, 25(4), pp.671-682
Wright, E. (2011) Risk management in public contracting. USA National Institute of Governmental Purchasing (under LEAP program
Yet, B., Constantinou, A., Fenton, N., Neil, M., Luedeling, E., Shepherd, K. (2016) A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study. Expert Systems with Applications, 60, pp.141-155