Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem

TL;DR

This paper develops a specialized genetic algorithm for nurse rostering by leveraging problem structure and co-evolutionary strategies, significantly improving solution quality over standard methods in real hospital data.

Contribution

It introduces a co-evolutionary genetic algorithm that incorporates problem-specific knowledge, incentives, and mutation operators tailored for nurse rostering.

Findings

Improved solution quality over canonical GA

Effective handling of scheduling constraints

Practical solutions demonstrated with real hospital data

Abstract

There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using co-operating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem