The Time-Band Approximation Model on Flight Operations Recovery Model Considering Random Flight Flying Time in China

2015 IEEE International Conference on Systems, Man, and Cybernetics ◽

10.1109/smc.2015.131 ◽

2015 ◽

Cited By ~ 1

Author(s):

Haiwen Xu ◽

Songchen Han ◽

Yong Zhang ◽

Jianguang Li

Keyword(s):

Approximation Model ◽

Recovery Model ◽

Flight Operations ◽

Random Flight ◽

Time Band

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Weighted Time-Band Approximation Model for Flight Operations Recovery considering Simplex Group Cycle Approaches in China

Mathematical Problems in Engineering ◽

10.1155/2016/3201490 ◽

2016 ◽

Vol 2016 ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Haiwen Xu ◽

Songchen Han

Keyword(s):

Numerical Experiments ◽

Recovery Period ◽

Computation Time ◽

Actual Data ◽

Approximation Model ◽

Hub And Spoke ◽

Control Center ◽

Flight Operations ◽

Operations Control ◽

Time Band

The time-band approximation model for flight operations recovery following disruption (Bard, Yu, Arguello, IIE Transactions, 33, 931–947, 2001) is constructed by partitioning the recovery period into time bands and by approximating the delay costs associated with the possible flight connections. However, for disruptions occurring in a hub-and-spoke network, a large number of possible flight connections are constructed throughout the entire flight schedule, so as to obtain the approximate optimal. In this paper, we show the application of the simplex group cycle approach to hub-and-spoke airlines in China, along with the related weighted threshold necessary for controlling the computation time and the flight disruption scope and depth. Subsequently, we present the weighted time-band approximation model for flight operations recovery, which incorporates the simplex group cycle approach. Simple numerical experiments using actual data from Air China show that the weighted time-band approximation model is feasible, and the results of stochastic experiments using actual data from Sichuan Airlines show that the flight disruption and computation time are controlled by the airline operations control center, which aims to achieve a balance between the flight disruption scope and depth, computation time, and recovery value.

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Automation Surprise

Aviation Psychology and Applied Human Factors ◽

10.1027/2192-0923/a000113 ◽

2017 ◽

Vol 7 (1) ◽

pp. 28-41 ◽

Cited By ~ 4

Author(s):

Robert J. de Boer ◽

Karel Hurts

Keyword(s):

Flight Control ◽

Representative Sample ◽

Aviation Safety ◽

Control Mode ◽

Cognitive Errors ◽

Laboratory Studies ◽

Airline Pilots ◽

Flight Operations ◽

Widespread Phenomenon ◽

Few Data

Abstract. Automation surprise (AS) has often been associated with aviation safety incidents. Although numerous laboratory studies have been conducted, few data are available from routine flight operations. A survey among a representative sample of 200 Dutch airline pilots was used to determine the prevalence of AS and the severity of its consequences, and to test some of the factors leading to AS. Results show that AS is a relatively widespread phenomenon that occurs three times per year per pilot on average but rarely has serious consequences. In less than 10% of the AS cases that were reviewed, an undesired aircraft state was induced. Reportable occurrences are estimated to occur only once every 1–3 years per pilot. Factors leading to a higher prevalence of AS include less flying experience, increasing complexity of the flight control mode, and flight duty periods of over 8 hr. It is concluded that AS is a manifestation of system and interface complexity rather than cognitive errors.

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