• The Journal of Information Systems
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• v.31 no.3
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• pp.177-195
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• 2022

Purpose This paper aims to prepare a full operational readiness by establishing an optimal flight plan considering the weather conditions in order to effectively perform the mission and operation of military aircraft. This paper suggests a flight prediction model and rules by analyzing the correlation between flight implementation and cancellation according to weather conditions by using big data collected from historical flight information of military aircraft supplied by Korean manufacturers and meteorological information from the Korea Meteorological Administration. In addition, by deriving flight rules according to weather information, it was possible to discover an efficient flight schedule establishment method in consideration of weather information. Design/methodology/approach This study is an analytic study using data mining techniques based on flight historical data of 44,558 flights of military aircraft accumulated by the Republic of Korea Air Force for a total of 36 months from January 2013 to December 2015 and meteorological information provided by the Korea Meteorological Administration. Four steps were taken to develop optimal flight prediction models and to derive rules for flight implementation and cancellation. First, a total of 10 independent variables and one dependent variable were used to develop the optimal model for flight implementation according to weather condition. Second, optimal flight prediction models were derived using algorithms such as logistics regression, Adaboost, KNN, Random forest and LightGBM, which are data mining techniques. Third, we collected the opinions of military aircraft pilots who have more than 25 years experience and evaluated importance level about independent variables using Python heatmap to develop flight implementation and cancellation rules according to weather conditions. Finally, the decision tree model was constructed, and the flight rules were derived to see how the weather conditions at each airport affect the implementation and cancellation of the flight. Findings Based on historical flight information of military aircraft and weather information of flight zone. We developed flight prediction model using data mining techniques. As a result of optimal flight prediction model development for each airbase, it was confirmed that the LightGBM algorithm had the best prediction rate in terms of recall rate. Each flight rules were checked according to the weather condition, and it was confirmed that precipitation, humidity, and the total cloud had a significant effect on flight cancellation. Whereas, the effect of visibility was found to be relatively insignificant. When a flight schedule was established, the rules will provide some insight to decide flight training more systematically and effectively.

• Journal of the Korea Society for Simulation
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• v.26 no.3
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• pp.35-46
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• 2017

Spare engine plays an important role for securing readiness of military strength during unexpected fault occurrences and field/depot planned maintenances. The purpose of this research is to present an approach towards the optimal number of spare engines/modules for diversity of aircraft types. We first reviewed two representative approaches, METRIC and meta model. We then investigated military aircrafts and categorized them into 5 types with regard to the engine type and number of engines/modules per aircraft. Through rigorous investigation of planned/non-planned maintenance of each type, we drew parameters and variables involved. As known, due to the complexity of the problem, it is impossible to develop a simple mathematical model with a closed form solution. Based on the airbase operation and maintenance logic with parameters/variable drawn, we developed a simulation model using ARENA well representing real field exercises. For the optimal solution, we applied OptQuest. It has shown that the program developed generates reliable results through a set of case examples.