Patent ID: 11961024
Assignee: INSIGHT DIRECT USA, INC.
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method for automatically generating baggage driver staffing recommendations, the method comprising:
receiving historical flight data describing a plurality of historical flights, the historical flight data comprising, for each flight of the plurality of historical flights, a historical driver quantity, a historical missed bag quantity, and historical flight parameters;
generating and labeling training data from a portion of the historical flight data and generating unlabeled test data from a remainder of the historical flight data;
iteratively training a computer-implemented machine learning model to generate a trained computer-implemented machine learning model configured to predict expected missed bag quantities based on driver quantities and flight parameters, wherein iteratively training the computer-implemented machine learning model comprises iteratively adjusting at least one parameter related to accuracy of the computer-implemented machine learning model based on the training data to iteratively adjust a fit of the computer-implemented machine learning model to the test data;
wherein iteratively adjusting includes:
testing the performance of the computer-implemented machine learning model to the test data to evaluate the fit of the model;
responsive to determining the fit is undesirable, further adjusting at least one parameter of the model to improve the fit; and
iteratively testing and adjusting until the fit of the model becomes desirable;

receiving flight parameters for a flight set, the flight set comprising a plurality of flights, and wherein each flight of the plurality of flights is scheduled to occur within a departure window;
generating a plurality of predictive staffing models by simulating missed bag quantities for a range of driver quantities using the trained computer-implemented machine learning model and the flight parameters, wherein each predictive staffing model relates predicted quantities of missed bags to quantities of staffed drivers for one flight of the plurality of flights;
automatically generating, using the plurality of predictive staffing models and a threshold quantity of missed bags, a plurality of recommended driver quantities, each recommended driver quantity corresponding to one flight of the plurality of flights and predicted to result in a quantity of missed bags not greater than the threshold quantity of missed bags;
generating a total recommended driver quantity based on the plurality of recommended driver quantities; and
modifying electronic data representative of driver schedules during the departure window based on the total recommended driver quantity, the electronic data stored by an electronic driver scheduling system.