Risk management throughout the project life cycle, from the initial phase beginning to the final phase, ended continuously and dynamically, is one of the basic requirements for the success of any project oriented organization. To effectively identify and manage risks at all stages of the project, careful selection must be made at all levels of the organization. In some cases, implementing the response may eliminate the risk but reciprocally create additional risks for the project. Most of the available risk selection methods mainly focus on eliminating the primary risks without understanding the secondary and residual risks that may arise during the project implementation. The occurrence of secondary risk can be a direct result of performing an activity that responds to a primary risk. This study proposes an optimization model to select risk response measures for primary and secondary risks. The difference between our proposed method and Zhao method (2018) will be that the proposed one does not lead to selecting a predetermined strategy. This method examines the two dimensions of time and costs in response to primary and secondary risks. In addition, the genetic metaheuristic algorithm has been used to solve the problem in the dimensions mentioned above.
Wang L, Sun T, Qian C, Goh M, Mishra VK. Applying social network analysis to genetic algorithm in optimizing project risk response decisions. Information Sciences. 2020;512:1024–42.
3.
Meyer T, Reniers G. Engineering Risk Management. 2022.
4.
Ou-Yang C, Chen WL. Applying a risk assessment approach for cost analysis and decision making: a case study for a basic design engineering project. Journal of the Chinese Institute of Engineers. 2017;40(5):378–90.
5.
Stulz RM. 5. Rethinking Risk Management. 2008. p. 87–120.
6.
Smith PG, Merritt GM. Proactive Risk Management.
7.
Khalilzadeh M, Ghasemi P, Afrasiabi A, Shakeri H. Hybrid fuzzy MCDM and FMEA integrating with linear programming approach for the health and safety executive risks: a case study. Vol. 16. 2021. p. 1025–53.
8.
Denisova AI. Assessment of Options for Substantiating an Infrastructure Project Implementation Plan in the Presence of Associated Risks. IOP Conference Series: Earth and Environmental Science. 2021;666(6):062075.
9.
Zhang H, Sun Q. An Integrated MCDM Approach to Train Derailment Risk Response Strategy Selection. Symmetry. 2020;12(1):47.
10.
Zhang Y, Zuo F, Guan X. Integrating case-based analysis and fuzzy optimization for selecting project risk response actions. Physica A: Statistical Mechanics and its Applications. 2020;545:123578.
11.
Piedade L, Silva J. Optimized communication plan and its impact on the contingency plan previously put in place for timely crisis response in the air sector. VII RIDITA-Air Transportation Sustainability: Technological, Operational, Economic, Social And Environmental Strategies. 1(VII RIDITA):465–73.
12.
Kuo RJ, Nugroho Y, Zulvia FE. Application of particle swarm optimization algorithm for adjusting project contingencies and response strategies under budgetary constraints. Computers & Industrial Engineering. 2019;135:254–64.
13.
Koulinas GK, Marhavilas PK, Demesouka OE, Vavatsikos AP, Koulouriotis DE. Risk analysis and assessment in the worksites using the fuzzy-analytical hierarchy process and a quantitative technique–A case study for the Greek construction sector. Safety Science. 2019;112:104–96.
14.
Adeleke AQ, Bahaudin AY, Kamaruddeen AM, Bamgbade JA, Salimon MG, Khan MWA, et al. The influence of organizational external factors on construction risk management among Nigerian construction companies. Safety and Health at Work. 2018;9(1):124–115.
15.
Soleh M, Arymurthy AM, Wiguna S. Change Detection In Multi-Temporal Images Using Multistage Clustering For Disaster Recovery Planning. Jurnal Ilmu Komputer dan Informasi. 2018;11(2):110–7.
16.
Huang X, Zhao T. Project selection and scheduling with uncertain net income and investment cost. Applied Mathematics and Computation. 2014;247:7–61.
17.
Vikhar PA. Evolutionary algorithms: A critical review and its future prospects. In: 2016 International conference on global trends in signal processing, information computing and communication (ICGTSPICC. 2016. p. 261–5.
18.
Rada VD, Martín VM. Random route and quota sampling: Do they offer any advantage over probably sampling methods? Open Journal of Statistics. 2014;ID(48774).
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