The Doctor Rostering Problem - Asser Fahrenholz

Chapter 4. Solving the DRP 28
This model, where only a variable for each day and shift is available, is different from
what is done in an optimal solver, i.e. GAMS. The difference lies in the approach of
solving the problem. In my implementation, each shift object is assigned a doctor object,
is chosen. In the GAMS model, a binary variable for every day, shift and doctor exists.
The schedule, being the main object of the implementation also contains the methods
for selecting the best doctor for a given shift or day. These two methods are included in
appendix A.5.
In the following sections, I describe the implementation of the construction heuristic and
the metaheuristics chosen in this project. It is important to note that the implemen-
tations share several methods and therefore are extending the same basic class. This
allows the classes to take advantage of Javas extend, allowing the heuristics to inherit
methods that they share from so called parent classes. This is depicted in figure 4.7.
4.4.2 A partial enumeration
Figure 4.7: Heuristics inheritance
In the construction heuristic, the option of enumerating the assignments of a whole day
of shifts, as opposed to only assigning one shift at a time, is available. The enumeration
algorithm produces all permutations of size k chosen from N elements and is adopted
from Sedgewick and Wayne [21] (which no longer seems to be available as of the time of
writing). Sedgewick and Wayne [21] originally created the class for use with characters
only. I adopted it into a version permuting a list of doctor objects instead. This
permutation class can be found in appendix A.5.2.