20.
Deployed US Navy aircraft carriers must stock a large number of spare parts to support the various types of aircraft embarked
on the ship. The sparing policy determines the spares that will be stocked on the ship to keep the embarked aircraft ready
to fly. Given a fleet of ten or more aircraft carriers and a cost of approximately 50 million dollars per carrier plus the
cost of spares maintained in warehouses in the United States, the sparing problem constitutes a significant portion of the
Navy’s resources. The objective of this work is to find a minimum-cost sparing policy that meets the readiness requirements
of the embarked aircraft. This is a very large, nonlinear, integer optimization problem. The cost function is piecewise linear
and convex while the constraint mapping is highly nonlinear. The distinguishing characteristics of this problem from an optimization
viewpoint are that a large number of decision variables are required to be integer and that the nonlinear constraint functions
are essentially “black box” functions; that is, they are very difficult (and expensive) to evaluate and their derivatives
are not available. Moreover, they are not convex. Integer programming problems with a large number of variables are difficult
to solve in general and most successful approaches to solving nonlinear integer problems have involved linear approximation
and relaxation techniques that, because of the complexity of the constraint functions, are inappropriate for attacking this
problem. We instead employ a pattern search method to each iteration of an interior point-type algorithm to solve the relaxed
version of the problem. From the solution found by the pattern search on each interior point iteration, we begin another pattern
search on the integer lattice to find a good integer solution. The best integer solution found across all interations is returned
as the optimal solution. The pattern searches are distributed across a local area network of non-dedicated, heterogeneous
computers in an office environment, thus, drastically reducing the time required to find the solution.
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