Risk Response Selection in Construction Projects
Abstract
Risk and its management is important for the success of the project, the risk management, which encompassed of planning, identification, analysis, and response has an important phase, which is risk response and it should not be undermined, as its success going to the projects the capability to overcome the uncertainty and thus an effective tool in project risk management, risk response used the collective information in the analysis stage and in order to take decision how to improve the possibility to complete the project within time, cost and performance. This stage work on preparing the response to the main risks and appoint the people who are responsible for each response. When it's needed risk response may be started in quantitative analysis stage and the repetition may be possible between the analysis and risk response stage. The aim of this research is to provide a methodology to make the plane for unexpected events and control uncertain situations and identify the reason for risk response failure and to respond to risk successfully by using the optimization method to select the best strategy. The methodology of this research divided into four parts, the first part main object is to find the projects whose risk response is failed, the second part includes the reasons for risk response Failure, the third part includes finding the most important risks generated from risk response that leads to increasing the cost of construction projects, the fourth part of the management system is selecting the optimal risk response strategy. An optimization model was used to select the optimal strategy to treat the risk by using Serval constraints such as the cost of the project, time of the project, Gravitational Search Algorithm and particle swarm used. The result of the risk response selection shows that The investment (contractor, bank) strategy shows a very good strategy as it saves the cost about 30%, while the Mitigate (pay for advances with interest 0. 1) Strategy show saving the cost 40% and giving land to contractors show saving the cost 40% finally the BIM strategy show saving the cost 25%. The risk response is an important part and should give a great attention and it must be used sophisticated method to select the optimal strategy, the two techniques both show high efficiency in selecting the strategy but Gravitational Search Algorithm show better performance.
Keywords
References
Choudhry, R., Aslam, M., Hinze, J., and Arain, F. “Cost and schedule risk analysis of bridge construction in Pakistan: Establishing risk guidelines.” J. Constr. Eng. Manage., 10.1061/(ASCE)CO.1943-(2014) 7862.0000857, 04014020.
Moini, N. “Modeling of risks threatening critical infrastructures: a System approach.” J. Infrastruct. Syst., (2015). 10.1061/(ASCE)IS.1943- 555X.0000263, 04015010
Guide, P. M. B. O. K4th Edition. Newton Square, Pennsylvania, USA: Project Management Institute. (2008)
Taylor, I and Bassler, J," Application of ANSI Standard to Space Station Resources”, Proceedings INOSE International the IEEE, .,(1997) 83(3):345{377, Mar 1995.
Tah, J. H. M., & Carr, V. A proposal for construction project risk assessment using fuzzy logic. Construction Management & Economics, (2000). 18(4), 491-500.
Nasirzadeh, F., Afshar, A., Khanzadi, M., & Howick, SIntegrating,"system dynamics and fuzzy logic modeling for construction risk management". Construction Management and Economics, (2008). 26(11), 1197-1212.
Ogunsanmi, O. E., Salako, O. A., & Ajayi, O. M. "Risk classification model for design and build projects". Journal of Engineering, Project, and Production Management, (2011). 1(1), 46
Polat, G., Okay, F., & Eray, E,"Factors affecting cost overruns in micro-scaled construction companies". Procedia engineering, (2014). 85, 428-435.
Kasimu, M. A,"Significant factors that cause cost overruns in building construction project in Nigeria". Interdisciplinary journal of contemporary research in business, (2012). 3(11), 775-780.
Hällgren, M., & Wilson, T.L. "Opportunities for learning from crises in projects, International Journal of Managing Projects in Business". (2011)
Fang, C., and Marle, F. “A simulation-based risk network model for decision support in project risk management.” Decis. Support Syst., (2012). 52(3), 635–644
Norris, C., Perry, J., & Simon, P. "Project risk analysis and management". The Association for Project Management, Buckinghamshire, UK, Retrieved March 31st, 2002, URL: http://www. euro logo. co. UK/apmrisksig/publications/mini prom. pdf.
syedhosini, S, M., Noori, S, & Hatefi, M, A. (2009). An integrated methodology for assessment and selection of the project risk response action, risk analysis, 29.
Hällgren, M., & Wilson, T.L. (2011). Opportunities for learning from crises in projects, International Journal of Managing Projects in Business.
MOTALEB, O., and KISHK, M. A," Risk Response Plan Framework for Housing Construction Project Delay in the UAE. COBRA", Proceedings of RICS, Arizona State University,(2012).
Zhang, Y., & Fan, Z. P," An optimization method for selecting project risk response strategies". International Journal of Project Management, (2014),32(3), 412-422.
Fan, M., Lin, N. P., & Sheu, C. "Choosing a project risk-handling strategy: An analytical model". International Journal of Production Economics, (2008). 112(2), 700-713.
Aven T., "The risk concept-historical and recent development trends, Reliability Engineering and System Safety "(99), 33–44, 2012
Veland H., Aven T., "Risk communication in the light of different risk perspectives", Reliability Engineering and System Safety 110 (2013) 34–40
KarimiAzari A., Mousavi N., Mousavi F., Hosseini S., "Risk assessment model selection in the construction industry, Expert Systems with Applications",2011 (38), 9105–9111.
Yang Y.C., "Risk management of Taiwan’s maritime supply chain security", Safety Science (49), 2011,382–393.
Mazouni M.H, Pour une Meilleure Approche du Management des Risques : De la Modélisation Ontologique du Processus Accidentel au Système Interactif d’Aide à la Décision, Thèse de Doctorat de l’Institut National Polytechnique de Lorraine, Université de Nancy, 2008.
Cheng T.C.E., Yip F.K., Yeung A.C.L., "Supply risk management via guanxi in the Chinese business context: The buyer’s perspective", International Journal of Production Economics (139), 3–13, 2012
Alhawari S., Karadsheh L., Talet A.N., Mansour E.," Knowledge-Based Risk Management framework for Information Technology project", International Journal of Information Management (32), (2012),50–65.
Standards Australia/Standards New Zealand Standard Committee. “Risk management–principles and guidelines.” AS/NZS ISO 31000: 2009, Sydney, NSW, Australia.
Perrenoud, A., Smithwick, J., Hurtado, K., and Sullivan, K. “Project risk distribution during the construction phase of small building projects.” J. Manage. Eng., (2015). 10.1061/(ASCE)ME.1943-5479 .0000417, 04015050
Jason Westland,"The Project Management Life Cycle". London, United Kingdom,(2006).
Govan, Paul, and Ivan Damnjanovic. "The resource-based view on project risk management." Journal of Construction Engineering and Management 142, no. 9 (2016): 04016034.Harvard.
Likhitruangsilp, V., and Ioannou, P. “Analysis of risk-response measures for tunneling projects.” Construction Research Congress 2012: Construction Challenges in a Flat World, H. Cai, A. Kandil
Rao, Singiresu S., and Singiresu S. Rao. "Engineering optimization: theory and practice." John Wiley & Sons, 2009.
Soofifard, R., and M. Gharib. "A NEW APPROACH TO PROJECT RISK RESPONSES SELECTION WITH INTER-DEPENDENT RISKS." International Journal of Engineering-Transactions B: Applications 30, no. 5 (2017): 720.Harvard
Soofifard, R., & Bafruei, M. K. "An optimal model for Project Risk Response Portfolio Selection (P2RPS)". (2017).
Kennedy J, and Eberhart R. "Particle swarm optimization", IEEE international conference on neural networks, Vol. IV, Piscataway, NJ, (1995) pp. 1942–1948.
Coello C, and Luna E. "Use of particle swarm optimization to design combinational logic Circuits", Tyrell A, Haddow P, Torresen J, editors. 5th International conference on Evolvable systems: from biology to hardware, ICES (2003). Lecture notes in computer science
Zheng Y, Ma L, Zhang L, Qian J. " Robust pid controller design using particle swarm optimizer", IEEE international symposium on intelligence control, (2003), pp. 974–979.
Abido M. "Optimal design of power system stabilizers using particle swarm optimization", IEEE Trans Energy Conversion, Vol. 17, No. 3, pp. 406–413. analytical model. International Journal of Production Economics (2002) 112, 700–713.
[37]Shi Y, and Eberhart R. (1998a), A modified particle swarm optimizer, IEEE international conference on evolutionary computation, IEEE Press, Piscataway, NJ, pp. 69–73
E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi) "GSA: A Gravitational Search Algorithm,” Information Sciences, , (2009)vol. 179, no. 13.
R. K. Khadanga and S. Panda, "Gravitational search algorithm for Unified Power Flow Controller based damping controller design", International Conference on Energy, Automation and Signal. (2011)
M.Gauci, Dodd, T. J., & Groß, R. "Why ‘GSA: a gravitational search algorithm’is not genuinely based on the law of gravity". Natural Computing, (2012). 11(4).
DOI: 10.28991/cej-030950
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