The Doctor Rostering Problem - Asser Fahrenholz

Chapter 8. Future considerations 55
in the solution, a value of the contribution to the objective function is stored (both
violations of hard constraints, but also the objective function value contribution).
Algorithm 8.1 outlines a possible implementation of a delta function for equation H0.
Algorithm 8.1 Delta function H0 - require: schedule S
∆H0 = 0
Set ← Select random shifts from S
for all Shifti in Set do
Shift2 ← S.getNextShift(Shifti)
if Shifti.getDoctor()= Shift2.getDoctor() then
∆H0 = ∆H0 + 1
end if
end for
Return ∆H0
∆H0 is then the amount of shifts in the new neighborhood that are violating constraint
H0. Similar rules could be devised for the rest of the constraints, keeping hard constraints
violations separated from soft constraints objective function value contribution.
The total ∆ (over all constraints) could then be compared to that of the contribution
made by the shifts affected by the neighborhood.
8.3 GRASP learning
A simple way to improve the GRASP algorithm used in this project would be to incor-
porate one or more of the learning mechanisms described in section 4.3.3. One way to do
this would be to track the (shift,doctor)-assignment that violates one or more rules and
bias that specific assignment such that GRASP would be more inclined not to perform
the same assignment in the next iteration.
Expanding on this, could be more biasing towards (shift,doctor)-assignments that violate
more or less rules than other (shift,doctor)-assignments. The bias-function could then
be introduced through a ranking, where a higher rank means less violations and higher
priority, or a probability distribution, with a probability for each assignment based on
the average number of rules it violated in the previous iterations of GRASP (and perhaps
how many violations it produces in the current).
8.3.1 GRASP stop criterion
An improvement, other than those presented in section 4.3.3, to the GRASP imple-
mentation is that of another stop criterion. As concluded in chapter 7, the GRASP