A Bayesian Optimisation Algorithm for the Nurse Scheduling Problem

TL;DR

This paper introduces a Bayesian optimization algorithm for nurse scheduling that explicitly learns and combines scheduling rules, demonstrating success on real data and potential applicability to other scheduling problems.

Contribution

The paper presents a novel Bayesian optimization approach that explicitly learns scheduling rule combinations for nurse scheduling, improving solution quality.

Findings

Successfully applied to 52 real data instances

Outperforms previous implicit learning methods

Potential applicability to other scheduling problems

Abstract

A Bayesian optimization algorithm for the nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurses assignment. Unlike our previous work that used Gas to implement implicit learning, the learning in the proposed algorithm is explicit, ie. Eventually, we will be able to identify and mix building blocks directly. The Bayesian optimization algorithm 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, ie 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.