Intelligence Surveillance and Reconnaissance Asset Assignment for Optimal Mission Effectiveness
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This research develops mathematical programming techniques to solve an intelligence, surveillance, and reconnaissance sensor assignment problem for USSTRATCOM. The problem as specifed is hypothesized to be difficult (i.e NP-HARD).With the smallest test cases, the true optimal solution is found using simple optimization techniques, but, due to intractability, the optimal solutions for larger test cases are not found using these same techniques. Instead, heuristic techniques are applied to several test cases in order to determine the best, robust methodologies to find true or near optimal soluti...
This research develops mathematical programming techniques to solve an intelligence, surveillance, and reconnaissance sensor assignment problem for USSTRATCOM. The problem as specifed is hypothesized to be difficult (i.e NP-HARD).With the smallest test cases, the true optimal solution is found using simple optimization techniques, but, due to intractability, the optimal solutions for larger test cases are not found using these same techniques. Instead, heuristic techniques are applied to several test cases in order to determine the best, robust methodologies to find true or near optimal solutions. Specifically, simulated annealing (SA) is tested for convergence properties across several different parameter settings. This research also utilizes local search techniques with simple exchange neighborhoods of various sizes. Mission prioritization is also examined via a weighted sum scalarization technique.
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