Corrective actions selection in the safety risk management process using mathematical modeling

Introduction: Risk assessment is a main tool in safety management process as it can help managers to choose corrective actions by providing appropriate information. The purpose of this paper was to select the optimal corrective actions among the proposed ones by the experts based on mathematical mod...

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Bibliographic Details
Main Authors: Morteza Cheraghi, Babak Omidvar, Ali Akbar Eslami-Baladeh, Hamid Reza Jafari, Ali Mohammad Younesi
Format: Article
Language:fas
Published: Tehran University of Medical Sciences 2018-09-01
Series:بهداشت و ایمنی کار
Subjects:
Online Access:http://jhsw.tums.ac.ir/article-1-5908-en.html
Description
Summary:Introduction: Risk assessment is a main tool in safety management process as it can help managers to choose corrective actions by providing appropriate information. The purpose of this paper was to select the optimal corrective actions among the proposed ones by the experts based on mathematical modeling, taking into account the standards and also the limitations including the cost. Material and Method: In this paper, a model was presented to find the optimal corrective actions regarding the organization goals (maximum in risk reduction value) and the limitations such as cost and level of acceptable risk. Due to extensive number of solutions, Genetic Algorithm (GA) is used for solving the problem. Result: To show the capability of this method in an industrial environment, a power generation industry with 40 hazards was considered as the case study. Then, the risk of hazards was estimated and corrective actions were determined for each of them. Using the proposed model, corrective actions were selected optimally, with the least possible cost; all risks were reduced below the level of organizational acceptable risk. Conclusion: It was shown that the optimal corrective actions using mathematical modeling are selected with high precision in acceptable time. This method is suggested as an alternative for conventional qualitative methods based on expert’s opinions.
ISSN:2251-807X
2383-2088