Rapid generation of minimum length pilot training schedules

• US 7,240,018 B2
• Filed: 01/11/2002
• Issued: 07/03/2007
• Est. Priority Date: 01/11/2002
• Status: Active Grant

• Current Assignee: Navitaire LLC (Amadeus Global Travel Distribution S.A.)
• Original Assignee: Navitaire Incorporated (Accenture PLC)
• Inventors: Thengvall, Benjamin Glover, Qi, Xiangtong
• Primary Examiner(s): Jeanty; Romain
• Application Number:
  US10/045,522
• Publication Number:
  US 20030139958A1
• Time in Patent Office: 1,999 Days
• Field of Search: 705/5, 705/7, 705/9
• US Class Current: 705/7.37
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/06314 Calendaring for a resource
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/109 Time management, e.g. calendars, reminders, meetings or time accounting
• CPC Class Codes: G06Q 10/1097 Task assignment
• CPC Class Codes: G06Q 50/2057 Career enhancement or continuing education service

Abstract

The present disclosure provides for a system for rapidly generating minimum length pilot training schedules which uses a branch and bound, and a mixed integer programming model with constraints to produce student and resource schedules at a device period level for all pilots of an airline.

Claims

1. A system for rapidly generating pilot training schedules for all pilots of an entire airline, which comprises:

user communication means for receiving user requests for said pilot training schedules and input data from a user;

optimization processor means in electrical communication with said user communication means for receiving said user requests and said input data, and in response to said user requests generating optimal pilot training schedules rapidly for all pilots of said entire airline from said input data, wherein said optimization processor generates a mixed integer programming model of an optimizer session as follows in response to said daily training schedule, and solves said mixed integer programming model to provide detailed optimal pilot training schedules;

2. The system of claim 1, wherein said input data includes identification of available training resources, available training instructors, classes that need to be scheduled, a class roster for each class to be scheduled, individual student training requirements, recurrent training requirements, individual student experience and qualifications, and training curriculum information.

3. The system of claim 2, wherein said optimization processor means executes a branch and bound algorithm to generate a daily training schedule for each of said classes by determining on which calendar days training will take place.

4. The system of claim 3, wherein said branch and bound algorithm generates plural branch and bound trees which are used in repeated cycles with each tree solving a larger subset of said classes to progressively refine said daily training schedule until all of said classes have been scheduled.

5. The system of claim 3, wherein said branch and bound algorithm generates plural branch and bound trees which are used in repeated cycles with each tree solving a larger subset of said classes to progressively refine said daily training schedule until allotted time for generating said daily training schedule has elapsed.

6. The system of claim 4, wherein each of said plural branch and bound trees is comprised of a root node, child nodes, and leaf nodes, and said optimization processor means compares said leaf nodes of each of said plural branch and bound trees to select said optimal training schedules.

7. The system of claim 1, wherein said optimal pilot training schedules include detailed assignment of resources for each day of training for each student in each class.