# A Component Based Heuristic Search Method with Evolutionary Eliminations

## TL;DR

This paper introduces a novel component-based heuristic search with evolutionary eliminations for nurse scheduling, decomposing schedules into components and iteratively improving them through natural selection and mutation-inspired strategies.

## Contribution

It presents a new method combining component decomposition with evolutionary elimination strategies to improve nurse rostering solutions.

## Key findings

- Effective on 52 real-world data instances
- Outperforms traditional heuristics in solution quality
- Demonstrates applicability to complex scheduling problems

## Abstract

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then to implement two evolutionary elimination strategies mimicking natural selection and natural mutation process on these components respectively to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs an evaluation function which evaluates how well each component contributes towards the final objective. Two elimination steps are then applied: the first elimination eliminates a number of components that are deemed not worthy to stay in the current schedule; the second elimination may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.

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Source: https://tomesphere.com/paper/0910.2593