An Indirect Genetic Algorithm for a Nurse Scheduling Problem

TL;DR

This paper presents an indirect genetic algorithm with heuristic decoding for nurse scheduling, demonstrating high-quality solutions that outperform a Tabu Search method in speed and flexibility based on real hospital data.

Contribution

The paper introduces a novel indirect genetic algorithm with heuristic decoding and hybrid crossover operators for nurse scheduling, improving solution quality and computational efficiency.

Findings

The proposed algorithm finds high-quality schedules efficiently.

It outperforms a recent Tabu Search approach in speed and flexibility.

Using bounds reduces solution space and enhances performance.

Abstract

This paper describes a Genetic Algorithms approach to a manpower-scheduling problem arising at a major UK hospital. Although Genetic Algorithms have been successfully used for similar problems in the past, they always had to overcome the limitations of the classical Genetic Algorithms paradigm in handling the conflict between objectives and constraints. The approach taken here is to use an indirect coding based on permutations of the nurses, and a heuristic decoder that builds schedules from these permutations. Computational experiments based on 52 weeks of live data are used to evaluate three different decoders with varying levels of intelligence, and four well-known crossover operators. Results are further enhanced by introducing a hybrid crossover operator and by making use of simple bounds to reduce the size of the solution space. The results reveal that the proposed algorithm is able to find high quality solutions and is both faster and more flexible than a recently published Tabu Search approach.