  Two ideas taken from Bayesian optimization and classifier systems are
presented for personnel scheduling based on choosing a suitable scheduling rule
from a set for each persons assignment. Unlike our previous work of using
genetic algorithms whose learning is implicit, the learning in both approaches
is explicit, i.e. we are able to identify building blocks directly. To achieve
this target, the Bayesian optimization algorithm builds a Bayesian network of
the joint probability distribution of the rules used to construct solutions,
while the adapted classifier system assigns each rule a strength value that is
constantly updated according to its usefulness in the current situation.
Computational results from 52 real data instances of nurse scheduling
demonstrate the success of both approaches. It is also suggested that the
learning mechanism in the proposed approaches might be suitable for other
scheduling problems.
