Establishing a Budget for Optimal Response Strategies for Fisks Categorized into Distinct Groups by using a Mathematical Model and Genetic Algorithm

Authors

  • Hiba Omer Aljorany Civil Engineering Department, University of Technology, Iraq
  • Ahmed Mohammed Raoof Mahjoob Civil Engineering Department, College of Engineering, University of Baghdad, Iraq
Volume: 14 | Issue: 3 | Pages: 14747-14753 | June 2024 | https://doi.org/10.48084/etasr.7526

Abstract

Construction projects may be subjected to various risks which must be identified, evaluated, and a suitable response to each risk must be determined. The risk response stage is a crucial and significant phase in risk management that requires particular attention. This paper proposes an effective mathematical model for determining the most suitable strategy and action in dealing with both primary and secondary risk events in different risk categories that may arise in a construction project. It also provides a method for estimating or forecasting the anticipated budget for a risk response plan. Another contribution of this study is the development of an innovative approach that combines binary programming with the genetic algorithm. The efficacy of the proposed methodology was examined by its implementation in a real geothermal project. The results demonstrated that the proposed framework serves as a useful tool to tackle the challenges related to the selection and optimization of risk response strategies, as well as setting an appropriate budget for the risk response plan. The suggested model can help decision-makers to assess the variety of viable risk response actions and strategies and arrive at a more well-informed decision.

Keywords:

primary risk, secondary risk, risk responses, modeling, Genetic Algorithm, construction projects, risk management

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How to Cite

[1]
H. O. Aljorany and A. M. R. Mahjoob, “Establishing a Budget for Optimal Response Strategies for Fisks Categorized into Distinct Groups by using a Mathematical Model and Genetic Algorithm”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14747–14753, Jun. 2024.

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