Abstract
There are a number of sources of randomness that arise in military airlift operations. However, the cost of uncertainty can be difficult to estimate, and is easy to overestimate if we use simplistic decision rules. Using data from Canadian military airlift operations, we study the effect of uncertainty in customer demands as well as aircraft failures, on the overall cost. The system is first analyzed using the types of myopic decision rules widely used in the research literature. The performance of the myopic policy is then compared to the results obtained using robust decisions that account for the uncertainty of future events. These are obtained by modeling the problem as a dynamic program, and solving Bellman’s equations using approximate dynamic programming. The experiments show that even approximate solutions to Bellman’s equations produce decisions that reduce the cost of uncertainty.
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