Approximate dynamic programming and aerial refueling

Abstract

Aerial refueling is an integral part of the United States military's ability to strike targets around the world with an overwhelming and continuous projection of force. However, with an aging fleet of refueling tankers and an indefinite replacement schedule the optimization of tanker usage is vital to national security. Optimizing tanker and receiver refueling operations is a complicated endeavor as it can involve over a thousand of missions during a 24 hour period, as in Operation Iraqi Freedom and Operation Enduring Freedom. Therefore, a planning model which increases receiver mission capability, while reducing demands on tankers, can be used by the military to extend the capabilities of the current tanker fleet. Aerial refueling optimization software, created in CASTLE Laboratory, solves the aerial refueling problem through a multi-period approximation dynamic programming approach. The multi-period approach is built around sequential linear programs, which incorporate value functions, to find the optimal refueling tracks for receivers and tankers. The use of value functions allows for a solution which optimizes over the entire horizon of the planning period. This approach varies greatly from the myopic optimization currently in use by the Air Force and produces superior results. The aerial refueling model produces fast, consistent, robust results which require fewer tankers than current planning methods. The results are flexible enough to incorporate stochastic inputs, such as: varying refueling times and receiver mission loads, while still meeting all receiver refueling requirements. The model's ability to handle real world uncertainties while optimizing better than current methods provides a great leap forward in aerial refueling optimization. The aerial refueling model, created in CASTLE Lab, can extend the capabilities of the current tanker fleet.