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The Doctor Rostering Problem - Asser Fahrenholz

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Chapter 7. Tests, results and discussion 50<br />

100<br />

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0<br />

SA-Day-1-2-R2-13%<br />

0 500 1.000 1.500 2.000 2.500<br />

Iterations<br />

SA-Day-1-2-R2-13%-vio SA-Day-1-2-R2-13%-value<br />

Working vio Working score Temperature<br />

(a) SA, on the output from figure 7.1(a)<br />

GRASP-Day-1-2-R2-18%<br />

0 500 1.000 1.500 2.000 2.500 3.000<br />

Iterations<br />

GRASP-Day-1-2-R2-18%-vio GRASP-Day-1-2-R2-18%-value<br />

Working vio Working score<br />

1.000<br />

(a) Test 5: GRASP, enumeration, R2, RCL 1<br />

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Temperature<br />

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Figure 7.2: SA performance: Test 0<br />

80<br />

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SA-Shift-1-2-R2-13%<br />

0 500 1.000 1.500 2.000 2.500<br />

Iterations<br />

SA-Shift-1-2-R2-13%-vio SA-Shift-1-2-R2-13%-value<br />

Working vio Working score Temperature<br />

(b) SA, on the output from figure 7.1(b)<br />

GRASP-Shift-1-2-R2-18%<br />

1.000<br />

GRASP-Shift-1-2-R2-18%-vio GRASP-Shift-1-2-R2-18%-value<br />

Working vio Working score<br />

Figure 7.3: GRASP performance: Test 5<br />

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0 500 1.000 1.500 2.000 2.500 3.000<br />

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(b) Test 5: GRASP, no enumeration, R2, RCL<br />

1<br />

To comment on the performance of SA, I shall be highlighting test 9 (the empty sched-<br />

ule). In figure 7.5 on the next page it is seen that SA is able to optimise both solutions<br />

to a degree where V(S) = 0.<br />

Observing the performance of SA on more constrained problems, such as test 2 and<br />

test 5, figure 7.5 on the following page also shows how constrained problems makes SA<br />

increase Z(S) as V(S) decreases.<br />

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Temperature

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