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Unformatted text preview: _D Flush_1 2 1.9859 Flush_2 2 1.8265 Flush_3 2 1.5965 Flush_4 2 1.7875 Gap_1 3 2.9106 Gap_2 3 2.8932 Gap_3 3 2.7987 Gap_4 3 2.7871 Table 2; Measure variation in optimal solution 123 z_E Time_D Time_E 25.1 0.67 79.9 24.5 0.11 37.7 24.2 0.17 37.2 23.9 0.25 26.2 23.6 0.12 20.4 # mea. 8 4 3 2 1 z_D 25.1 24.5 24.2 23.9 23.6 Table 3; A Comparison The solution satisfies the constraints on the critical measures. In addition, it chooses tolerances in a way such that the accumulated measure is close to the defined limits. This is expected, since larger tolerances are associated with lower costs. Table 3 shows the optimal solution and the calculation times for the proposed algorithm (z_D). It also shows the exhaustive search (z_E) on a series of tests containing different numbers of measures. The optimal solutions are the same for the two algorithms as expected. The big difference is the time it takes to evaluate the solution. For example, the proposed algorithm for eight measures is about 120 times faster than exha...
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This note was uploaded on 08/03/2010 for the course DD 1234 taught by Professor Zczxc during the Spring '10 term at Magnolia Bible.

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