A Component Based Heuristic Search method with Adaptive Perturbations for Hospital Personnel Scheduling

TL;DR

This paper introduces a novel component-based heuristic search with adaptive perturbations for nurse scheduling, effectively improving solutions by dynamically evaluating and modifying schedule components in real-world hospital scenarios.

Contribution

It presents a new adaptive perturbation heuristic that decomposes schedules into components and iteratively improves them using dynamic evaluation and constructive heuristics.

Findings

Successfully applied to 52 real-world data instances

Demonstrates effectiveness in hospital nurse scheduling

Outperforms traditional methods in solution quality

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 adaptive perturbations, 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 mimic a natural evolutionary process on these components 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 a dynamic evaluation function which evaluates how well each component contributes towards the final objective. Two perturbation steps are then applied: the first perturbation eliminates a number of components that are deemed not worthy to stay in the current schedule; the second perturbation 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.