Personnel Scheduling Using Flight Segment-Specific Time Buffers
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Solution Overview
Problem
Current personnel scheduling systems often exceed personal limits of personnel, such as pilots, due to inadequate buffers for time variances, leading to inefficiencies and wasted crew time, as they rely on general buffers rather than flight segment-specific variances.
Innovation Solution
A computer-implemented method that retrieves historical data to determine specific buffers for flight segments based on likelihoods of time variances, allowing for personalized itineraries that do not exceed personal limits by applying reliability factors to predict and manage time variances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If general buffers are used for personnel scheduling, then the scheduling process is simple, but personnel limits are frequently exceeded due to inadequate time variance coverage
Solution Approach 1:
The patent applies local quality by transitioning from uniform general buffers to flight segment-specific buffers. Each flight segment receives a customized buffer based on its historical time variance characteristics, allowing the scheduling system to account for local variations in operational behavior rather than applying a one-size-fits-all approach.
Solution Approach 2:
The system performs preliminary action by pre-calculating and storing buffer values for each flight segment based on historical data analysis before actual scheduling occurs. This advance preparation enables the scheduling algorithm to automatically incorporate appropriate time variances without requiring real-time complex calculations during the scheduling process.
2Reliability
If flight segment-specific buffers are implemented, then time variance management improves, but computational complexity increases
Solution Approach 1:
The system performs preliminary action by pre-calculating and storing buffer values for each flight segment based on historical data analysis before actual scheduling occurs. This advance preparation enables the scheduling algorithm to automatically incorporate appropriate time variances without requiring real-time complex calculations during the scheduling process.
Solution Approach 2:
The patent applies segmentation by dividing the scheduling problem into discrete flight segment-level components. Each segment is independently analyzed and assigned its own buffer based on historical performance, allowing the complex overall scheduling problem to be broken down into manageable, independent units that can be processed systematically.
3Measurement precision
If historical data analysis is performed for each flight segment, then buffer accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-calculating and storing buffer values for each flight segment based on historical data analysis before actual scheduling occurs. This advance preparation enables the scheduling algorithm to automatically incorporate appropriate time variances without requiring real-time complex calculations during the scheduling process.
Solution Approach 2:
The patent applies copying by utilizing historical data patterns and statistical relationships from past flight segments to infer and apply buffer values to current scheduling scenarios. This approach copies the temporal patterns and variance characteristics from historical records to inform future scheduling decisions, reducing the need for real-time analysis.
Data Source
AI summary
Techniques for assigning various operations to itineraries that can be assigned to personnel. A plurality of tags relating to past instances of a plurality of various operations are retrieved from an electronic data source. Each tag includes a first symbol representing a magnitude of time variance for a respective operation of the various operations and a second symbol representing a likelihood of time variance for the respective operation. The plurality of tags are parsed, and a plurality of predicted time variances are determined. A first predicted time variance is selected for a first operation based on a reliability factor. A combination of operations is identified, and a likelihood that the combination of operations does not exceed at least one personal limit of personnel is determined. An itinerary that includes the identified combination of operations is generated. Personnel are automatically tasked to perform the operations of the generated itinerary.