  Schedules can be built in a similar way to a human scheduler by using a set
of rules that involve domain knowledge. This paper presents an Estimation of
Distribution Algorithm (eda) for the nurse scheduling problem, which involves
choosing a suitable scheduling rule from a set for the assignment of each
nurse. Unlike previous work that used Genetic Algorithms (ga) to implement
implicit learning, the learning in the proposed algorithm is explicit, i.e. we
identify and mix building blocks directly. The eda is applied to implement such
explicit learning by building a Bayesian network of the joint distribution of
solutions. The conditional probability of each variable in the network is
computed according to an initial set of promising solutions. Subsequently, each
new instance for each variable is generated by using the corresponding
conditional probabilities, until all variables have been generated, i.e. in our
case, a new rule string has been obtained. Another set of rule strings will be
generated in this way, some of which will replace previous strings based on
fitness selection. If stopping conditions are not met, the conditional
probabilities for all nodes in the Bayesian network are updated again using the
current set of promising rule strings. Computational results from 52 real data
instances demonstrate the success of this approach. It is also suggested that
the learning mechanism in the proposed approach might be suitable for other
scheduling problems.
