A Multiple Ant Colony Metaheuristic for the Air Refueling Tanker Assignment Problem
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A key tenet to the Air Force's vision of Global Vigilance, Reach, and Power is the ability to project power via the use of aerial refueling. Scheduling of limited tanker resources is a major concern for Air Mobility Command (AMC). Currently the Combined Mating and Ranging Planning System (CMARPS) is used to plan aerial refueling operations, however due to the complex nature of the program and the length of time needed to run a scenario, the need for a simple tool that runs in much shorter time is desired. Ant colony algorithms are recently developed heuristics for finding solutions to difficul...
A key tenet to the Air Force's vision of Global Vigilance, Reach, and Power is the ability to project power via the use of aerial refueling. Scheduling of limited tanker resources is a major concern for Air Mobility Command (AMC). Currently the Combined Mating and Ranging Planning System (CMARPS) is used to plan aerial refueling operations, however due to the complex nature of the program and the length of time needed to run a scenario, the need for a simple tool that runs in much shorter time is desired. Ant colony algorithms are recently developed heuristics for finding solutions to difficult optimization problems based on simulation the foraging behavior of ant colonies. They are distributive metaheuristics that combine an adaptive memory function with a local heuristic function to repeatedly construct possible solutions which can then be evaluated. Using multiple ant colony heuristics combined with a simple scheduling algorithm and modeling the Tanker Assignment Problem as a modified Multiple Depot Vehicle Routing Problem, an Excel based spreadsheet tool was developed which generates very good solutions in very short time.
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