The Doctor Rostering Problem - Asser Fahrenholz

Chapter 7. Tests, results and discussion 45
Imported shifts
From table 7.3 on the previous page, we can see that test instance 5 with Cons = 18 has
an average metaheuristic output of (251,20), which then decreases, over the results of
e.g. test instance 2 with (164,12), to test instance 9 with (4,2). It clearly does make the
problem harder when we add night shifts, evening shifts and wishes. It would have been
interesting to investigate what amount of these shifts, for each doctor, that is needed,
before feasibility cannot be guaranteed. Unfortunately, there was not time to do so.
Amount of doctors
When comparing test 0 (196,14), 2 (164,12), 3 (258,18) and 6 (109,6), we see the same
results as we did for the effect of varying amounts of imported shifts. There is then the
problematic result of test 7, which actually comes out with the highest average V(S) out
of the four (246,22). This could be due to the instance containing some periods of time
that are over constrained, such that no amount of optimisation could solve that period
to feasibility.
It is peculiar that test 2 and 6 produces the results that they do considering that the
cons of test 6 is higher than that of test 2. I propose that the length of the schedule is
also a factor in how hard the problem is to solve.
Schedule length
When the length of the schedule varies, we see an immediate effect on the numbers in
table 7.3 on the preceding page. Test 4 (349,32) , 6 (109,6) and 7 (246,22) varies the most
in length and also varies greatly in average values. With test instance 4 being the longest,
the test also produces the worst results, contrary to test 6 that actually has a higher Cons
but produces far better results. I conclude that the length of the schedule has great effect
on the average results of tests. This is actually not unexpected, as the implementation
of the neighborhoods is such, that the transformation is done on a random set of shifts.
When the length of the schedule increases, the ”length” between the shifts that are
invalidating constraints also increase. Had there been implemented a form of guiding
mechanism for the neighborhood transformation, targeting the transformations at shifts
that are invalidating one or more constraints, I suspect the results would have been
different from what we see here.