The Application of Bayesian Optimization and Classifier Systems in Nurse Scheduling

TL;DR

This paper explores the use of Bayesian optimization and classifier systems for nurse scheduling, enabling explicit learning of scheduling rules and demonstrating success on real data instances.

Contribution

It introduces explicit learning methods using Bayesian networks and classifier systems for personnel scheduling, contrasting with previous implicit genetic algorithm approaches.

Findings

Successful application to 52 real nurse scheduling instances

Both methods effectively identify useful scheduling rules

Learning mechanisms may be applicable to other scheduling problems

Abstract

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.