A tabu search approach to the strategic airlift problem

Air Mobility Command (AMC) planners currently use simulation systems or large-scale linear programming (LP) models in studying the strategic airlift problem. Simulations are descriptive in nature and therefore cannot prescribe optimal flight schedules. Aggregation is used in large-scale LP models to make the problem tractable and thus much operational level detail is lost. AMC planners need a tool which prescribes good solutions while maintaining the operational level detail necessary to produce flight schedules. This research outlines a robust algorithm that obtains excellent solutions to the strategic airlift problem that possess the operational level detail necessary for AMC planners to develop the detailed routing and scheduling of strategic airlift aircraft. The algorithm utilizes the tabu search methodology.