Document Type : Research Article


1 Management Department, Islamic Azad University, Semnan Branch, Semnan, Iran.

2 Management Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran.


Any risk response strategy (RRS) tends to change the risk status in the project. Trivially, the response is designed to improve the risk status, but it may not necessarily work as planned or the outcome may extend beyond the effect of the specific risk for which the response was planned. There are cases where implementing a response eliminates the risk but reciprocally arises other risks for the project. Most of the existing RRSs are focused on eliminating the primary risks without considering the secondary and residual risks that may arise during the implementation stage. This is while secondary risks can be a direct result of performing an activity that is originally designed to respond to a primary risk. It is then evident that determining an appropriate set of measures for responding to risks plays an important role in the success of a project. In addition, it is important to note that a secondary risk that arises from implementing an RRS to a primary risk must be treated in a similar way to the primary risk itself, because, similar to primary risks, secondary and residual risks impose negative impacts on the project performance and hence must be responded adequately. Sometimes, responding to a primary risk results in such a serious secondary risk that makes the situation even worse than it was before implementing the response to the primary risk. Therefore, considering secondary and residual risks along with the primary risks is a vital step toward successful implementation of a project. In this study, an optimization model was proposed for RRS selection against primary and secondary risks. Compared to the model proposed by Zhao (2018), the present work offers a core novelty that prevents the selection of predefined strategies while considering two dimensions when formulating responses to primary and secondary risks, namely time and cost. Moreover, as a metaheuristic method, genetic algorithm was herein used to solve large-scale problems.


Main Subjects

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