Air Force Operations Center Scheduling

Project Summary

U.S. Air Force operations require staffing several operation centers with trained and certified personnel. Scheduling the staffing of these operation centers is currently a time consuming manual process. The schedules must be prepared for the operation centers, training events, training resources, and trainers necessary to maintain current certification. The goal of this project is to develop an optimization scheduling model that will enable the automation of the scheduling tasks, currently performed manually, for a USAF group-level organization.

The project team developed an optimization model that provides for the automatic scheduling of fifteen operation centers. The optimization model also schedules required training, training resources and instructors. The model is capable of handling daily changes and producing a re-optimized solution while still adhering to all objective and constraints.
The emphasis of the project was to develop, test and analyze an optimization algorithm to improve efficiency and performance over the existing manual scheduling process. The scope of the project was limited, and no user interface or software code to interface with the current scheduling tool, TimePiece, was developed.

Design and development of the optimization algorithm, the model, was conducted through an iterative approach. The model was developed using Linear Programming and Integer Programming techniques. Team AFOCS used AIMMS v3.11 as the primary software tool.

The optimization model produces monthly schedules for personnel in four phases. The first phase focuses on scheduling the shifts for the operation centers, as fulfilling shift demand is the first priority. After shifts are scheduled, the model’s second phase focuses on scheduling training events for each person to meet the monthly training requirements. The training events require personnel being trained, instructors to perform the training, and the necessary training resources, with each component having limited availability and potential scheduling conflicts. The third phase of the model entails scheduling the backup crew for each day. The fourth phase performs the assignment of students, instructors, and evaluators to the simulator training and evaluation events. After creation of the monthly original schedule for personnel, the last portion of the model development was to create the capability to reschedule mid-month due to an absence of one or more personnel.

The optimization algorithm was tested iteratively throughout the model development. Final testing was accomplished with simulated data provided by the project sponsors. The automated model provides a solution after about three hours of runtime. The model performs in much less time than the current scheduling process, which is approximately 1.5 weeks. Not only does the model reduce the scheduling process time down significantly, it also finds a solution within 2% of the relaxed linear programming solution’s optimality.