- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Aviation is a key industrial sector for global development. Safety is essential for its healthy growth. However its management is pervaded by simplistic methods based on risk matrices. We provide here a framework for risk management decisions in aviation safety at state level. This helps us in identifying the best portfolio that a state agency may implement to improve aviation safety in a country. We illustrate our proposal with a case study.
In striking contrast with the technological sophistication achieved in the aviation system from the aeronautical engineering perspective, risk management in AS is pervaded by unsophisticated methods evolving around the concept of risk matrix see  and , with its potential pitfalls. We have proposed a methodology for risk management in AS based on sound principles of risk and decision analysis . Its main advantages are providing an integrated coherent framework for safety resource allocation taking advantage of all available information, both from data and expert judgment. We also support risk monitoring, reporting and screening. We present two versions of the general model, stochastic and deterministic, to be implemented depending on the level of accuracy required and the available computational resources. The methodology is useful in defining the countermeasures that allow us to manage the resources referred to in , minimizing the risks associated with AS, taking into account various constraints (economic, technical, logistic,. . . ) over such resources. We have illustrated the methodology with a simplified example. On the other hand, the approach is much more technical and sophisticated than the above mentioned risk matrix based methods. We have countered this partly by training engineers in charge of implementing in practice the methodology, partly by developing RIMAS, a decision support system implementation of the proposed methodology. Beyond these, future work includes improving the occurrence forecasting methodology with the aid of SGDLMs from ; monitoring the implementation of the methodology to evaluate its actual impact in AS and eventually improve it and RIMAS; and, finally, extending it to include data from Flight Data Monitoring systems to improve occurrence predictions. Moreover, for the most worrisome occurrences we should undertake detailed studies, possibly through causal networks as in .