Reserve Crew Forecasting via Interquartile Range Analysis
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Solution Overview
Problem
The challenge in airline flight planning is accurately scheduling reserve crew members to avoid flight disruptions due to crew shortages while minimizing economic losses from overestimating the number of reserve crew needed, leading to unused reserves when the estimate is too high.
Innovation Solution
A system and method that use historical data to determine a forecasted reserve quantity for scheduling reserve crew members by calculating an interquartile range (IQR) or transformed interquartile range (IQRT) based on actual flight and reserve usage data, and transmit this quantity to a crew rostering system for scheduling, ensuring optimal reserve allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the estimated quantity of crew members assigned to reserve duty is increased to avoid flight disruptions, then flight reliability is improved, but economic loss increases due to unused reserves
Solution Approach 1:
The system changes the parameter of reserve quantity estimation from a static, conservative approach to a dynamic, data-driven approach. By using historical data and statistical methods (IQR, IQRT) to calculate optimal reserve quantities, the system adjusts the reserve parameter to match actual operational needs, thereby improving flight reliability while reducing economic loss from overstaffing.
Solution Approach 2:
The system implements feedback by continuously analyzing historical data on actual reserve usage and flight disruptions. This feedback loop allows the system to learn from past performance and refine reserve quantity estimates, ensuring that sufficient reserves are maintained for reliability while minimizing excess reserves that cause economic loss.
2Loss of energy
If the estimated quantity of crew members assigned to reserve duty is decreased to reduce economic loss, then cost efficiency is improved, but flight disruption risk increases due to crew shortage
Solution Approach 1:
The system transforms the reserve quantity parameter from a conservative estimate to a precisely calculated value based on historical data analysis. By using statistical methods to determine the optimal reserve quantity, the system reduces the parameter to the minimum necessary level, improving cost efficiency while maintaining adequate coverage for flight reliability.
Solution Approach 2:
The system performs preliminary analysis of historical data and statistical calculations before determining the reserve quantity. This advance preparation allows the system to identify the optimal reserve level that balances cost efficiency with flight reliability requirements, avoiding both overstaffing and understaffing.
3Reliability
If traditional conservative estimation methods are used to determine reserve quantity, then flight disruption risk is reduced, but unused reserves increase causing economic loss
Solution Approach 1:
The system changes the estimation parameter from a conservative fixed value to a dynamically calculated value based on historical data and statistical methods. This parameter transformation reduces unused reserves by aligning the reserve quantity with actual operational needs while maintaining flight reliability through data-driven decision making.
Solution Approach 2:
The system replaces the mechanical/conventional estimation method with a data-driven computational approach. By substituting traditional conservative estimation with statistical analysis and historical data processing, the system eliminates the need for excessive reserves while ensuring flight reliability through more accurate predictions.
Data Source
AI summary
A reserve system and method for determining a quantity of forecasted reserve for a crew-grouping for a quantity of scheduled flights in a future time period is disclosed. The reserve system may comprise a controller configured to receive a crew-grouping and a quantity of scheduled flights for an aircraft in a future time period, and a historical data set associated with the crew-grouping for a previous time period. The controller is further configured to determine a quantity of forecasted reserve based on: an interquartile range of values in the historical data set for a quantity of actual used reserve of the crew-grouping per day for the aircraft; or the quantity of scheduled flights per day in the future time period and a transformed interquartile range of a plurality of data points associated with the historical data set; and to transmit the quantity of forecasted reserve to a crew rostering system.