The Doctor Rostering Problem - Asser Fahrenholz

Chapter 2. The Doctor Rostering Problem 6
Resende and Ribeiro [20] and Resende and Feo [19] considers the Greedy Random-
ized Adaptive Search Procedure (GRASP). The GRASP described by the authors is
the main source of inspiration for the metaheuristic chosen in this project. They also
implement various improvements to their GRASP, incl. reactive GRASP, automated
RCL adjustments, variable neighborhoods, path-relinking, optimising memory-usage as
well as allowing for efficient cooperative parallel strategies. They show that the use of
GRASP along with these improvements can significantly improve the time required to
find a near-optimal solution compared to basic GRASP. Where Resende and Ribeiro
[20] investigates the use of GRASP on the set covering problem, Resende and Feo [19]
covers the effect of various hybridisations to GRASP.
Burke et al. [5] reviews the state of Nurse Scheduling Problem research. In their review,
they describe how different authors have approached the NSP, including bibliographic
reviews and research papers on various algorithms for solving the NSP. Silvestro and
Silvestro [22] argues that self-scheduling
. . . can easily lead to over- or understaffing, that the schedule is made for
the convenience of staff, and that there are no formal procedures for conflict
solving.
Opposite to this, Hung [14] argues that self-scheduling leads to greater staff-satisfaction
and improves co-operation. Both are different approaches to viewing the subject. Sil-
vestro and Silvestro [22] concludes that the benefits of self-scheduling relies on the size
and complexity of the problem. It can work well in smaller wards where the constraints
remain relatively simple. Burke et al. [5] also notes how finding the optimal solution is
largely meaningless, when most hospital administrators want to get high quality results
in a short amount of time. The administrators prefer quick generation of schedules that
satisfy all hard constraints and as many soft constrains as possible. Another observation
made by Burke et al. [5] is that optimal solutions in the literature tend to simplify the
problem, making them inapplicable for real world problems.
In their section covering TS, they note how Dowsland [8] allows search to go back and
forth from the feasible region, which is an important feature as todays problems in large
health care institutions tend to be nearly impossible to solve, while maintaining fea-
sibility. Contrary to this, Burke et al. [3] develops a model in which search remains
in the feasible part of the solution space. They describe PLANE, a commercial nurse
rostering system which employs a hybrid TS, embodying a TS coupled with either a
diversification mechanism or a shuffling technique to model human scheduling behavior.
Comparing their hybrid algorithm to a basic Steepest Descent (SD) search- or TS al-
gorithm, they find that their hybrid search algorithm performs overall better than the