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The Doctor Rostering Problem - Asser Fahrenholz

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Chapter 7. Tests, results and discussion 52<br />

250<br />

200<br />

150<br />

100<br />

50<br />

0<br />

SA-Shift-1-10-R2-12%<br />

0 500 1.000 1.500 2.000 2.500<br />

Iterations<br />

SA-Shift-1-10-R2-12%-vio SA-Shift-1-10-R2-12%-value<br />

Working vio Working score Temperature<br />

(a) Test 2: SA, R2, RCL 1%<br />

1.000<br />

900<br />

800<br />

700<br />

600<br />

500<br />

400<br />

300<br />

200<br />

100<br />

Temperature<br />

100<br />

80<br />

60<br />

40<br />

20<br />

0<br />

SA-Day-Fifty-2-R2-18%<br />

0 500 1.000 1.500 2.000 2.500<br />

Iterations<br />

SA-Day-Fifty-2-R2-18%-vio SA-Day-Fifty-2-R2-18%-value<br />

Working vio Working score Temperature<br />

Figure 7.6: SA performance: Test 2 and 5<br />

(b) Test 5: SA, R2, RCL 50%<br />

fact that the Z(S) can increase, sometimes ten-fold, as SA makes V(S) decrease towards<br />

0.<br />

Secondly, an use-case evaluation, given the test results in this chapter, brings me to<br />

the conclusion that GRASP with enumeration, RCL 1-5 % and a number of iterations<br />

between 5000 and 10000, would yield the best results of all possible combinations.<br />

Had there been time to do so, it would have been of great importance to test following:<br />

• Solving test-instances with GAMS, yielding proven bounds and comparing the<br />

heuristic solutions to these bounds.<br />

• How the initial temperature and update of the temperature affects the performance<br />

of SA.<br />

• How a skew distribution of weights would have affected the results of both GRASP<br />

and SA.<br />

1.000<br />

900<br />

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500<br />

400<br />

300<br />

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100<br />

Temperature

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