Under the warfare and other emergent environment, military vehicle scheduling and planning is subject to complex sets of objectives and constraints and thus is difficult to achieve high-quality solutions. The paper proposes a distributed computing framework to support effective military vehicle scheduling, in which asynchronous teams of intelligent agents cooperate with each other to produce a set of nondominated solutions that show the tradeoffs between objectives, and evolve a population of solutions towards a Pareto-optimal frontier. Experimental results demonstrate the capability and effectiveness of our approach.
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