A Mathematical modeling of project risk response according to primary, secondary, and residual risks under conditions of uncertainty using the Tabu search algorithm

Document Type : Research Paper


1 Department of Industrial Management, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

2 Department of Mathematics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran


Today, the uncertainty in the estimated time and cost of industrial projects is considered as an important challenge in the science of project management. If risk management is done regularly to identify potential problems and find their solution, it will easily complement other processes such as organizing, planning, budgeting, and cost control. In this regard, one of the most important and effective solutions to this problem is risk analysis (primary, secondary, and residual). In this research, an optimization model has been proposed to select actions to respond to risk for all three primary, secondary and residual risks. This research is quantitative. In building the model, the objective function is to minimize the total risk costs and the costs of reducing the time constraints applied to the relationship between two activities. Then, by determining a suitable reasonable time for the whole project and solving the model, an optimal set of actions to respond to the risks is determined. The basic innovation of this research, which does not cause the selection of a predetermined strategy, is the two limitations that examine the two dimensions of time and cost in response to primary and secondary risk. The results indicate that the initial risk costs have decreased. Also, by responding to the primary risk, secondary risks were created, which imposed a cost on the system, but this cost was reduced by assigning secondary strategies, as well as the optimal cost of activity failure with the sensitivity analysis that was done, the maximum amount of time that the project can end It was equal to 78 days and more than that makes the cost of failure of activities to be zero. Also, in this research, the genetic meta-heuristic algorithm and the Particle swarm algorithm were used to solve the problem in high dimensions, and the results showed that there is no difference in the results of these two algorithms.


Main Subjects

Abolghasemian, M., Pourghader Chobar, A., AliBakhshi, M., Fakhr, A., & Moradi Pirbalouti, S. (2021). Delay scheduling based on discrete-event simulation for construction projects. Iranian Journal of Operations Research, 12(1), 49-63.
Asgari, T., Daneshvar, A., Chobar, A. P., Ebrahimi, M., & Abrahamyan, S. (2022). Identifying key success factors for startups With sentiment analysis using text data mining. International Journal of Engineering Business Management, 14, 18479790221131612.
Bayanati, M., Peivandizadeh, A., Heidari, M. R., Foroutan Mofrad, S., Sasouli, M. R., & Pourghader Chobar, A. (2022). Prioritize Strategies to Address the Sustainable Supply Chain Innovation Using Multicriteria Decision-Making Methods. Complexity, 2022.
Chobar, A. P., Adibi, M. A., & Kazemi, A. (2022). Multi-objective hub-spoke network design of perishable tourism products using combination machine learning and meta-heuristic algorithms. Environment, Development and Sustainability, 1-28.
Feng, Y., Guo, X., Wei, B., & Chen, B. (2021). A fuzzy analytic hierarchy process for risk evaluation of urban rail transit PPP projects. Journal of Intelligent & Fuzzy Systems, 41(4), 5117-5128.
Ghasemi, P., Hemmaty, H., Pourghader Chobar, A., Heidari, M. R., & Keramati, M. (2022). A multi-objective and multi-level model for location-routing problem in the supply chain based on the customer’s time window. Journal of Applied Research on Industrial Engineering.
Gillis, M., Urban, R., Saif, A., Kamal, N., & Murphy, M. (2021). A simulation–optimization framework for optimizing response strategies to epidemics. Operations Research Perspectives, 8, 100210.
Liu, E., Barker, K., & Chen, H. (2022). A multi-modal evacuation-based response strategy for mitigating disruption in an intercity railway system. Reliability Engineering & System Safety, 223, 108515.
Pourghader Chobar, A., Sabk Ara, M., Moradi Pirbalouti, S., Khadem, M., & Bahrami, S. (2021). A multi-objective location-routing problem model for multi-device relief logistics under uncertainty using meta-heuristic algorithm. Journal of Applied Research on Industrial Engineering.
Tantri, F., & Amir, S. (2022). Optimizing Response Strategies of Healthcare System in a Large-scale Disaster. Journal of Safety Science and Resilience, 3(4), 288-301.
Touti, E., & Chobar, A. P. (2020). Utilization of AHP and MCDM integrated methods in urban project management (A case study for eslamshahr-tehran). International journal of industrial engineering and operational research, 2(1), 16-27.
Wang, L., Goh, M., Ding, R., & Pretorius, L. (2019). Improved simulated annealing based risk interaction network model for project risk response decisions. Decision support systems, 122, 113062.
Wang, L., Sun, T., Qian, C., Goh, M., & Mishra, V. K. (2020). Applying social network analysis to genetic algorithm in optimizing project risk response decisions. Information Sciences, 512, 1024-1042.
Zhang, Y., Zuo, F., & Guan, X. (2020). Integrating case-based analysis and fuzzy optimization for selecting project risk response actions. Physica A: Statistical Mechanics and Its Applications, 545, 123578.Zhang, Zhang, Y., & Zuo, F. (2016). Selection of risk response actions considering risk dependency. Kybernetes, 45(10), 1652-1667.
Zuo, F., & Zhang, K. (2018). Selection of risk response actions with consideration of secondary risks. International Journal of Project Management, 36(2), 241-254.