Roster Evaluation Based on Classifiers for the Nurse Rostering Problem

TL;DR

This paper introduces a machine learning classifier to speed up solution evaluation in heuristic algorithms for the complex nurse rostering problem, achieving significant efficiency gains without sacrificing solution quality.

Contribution

It presents a novel ML-based evaluation method integrated into heuristic search, improving speed while maintaining solution quality for nurse rostering problems.

Findings

Significant reduction in evaluation time during heuristic search.

Boosting algorithms enhance classifier accuracy and solution quality.

Approach effective on standard and real-world benchmarks.

Abstract

The personnel scheduling problem is a well-known NP-hard combinatorial problem. Due to the complexity of this problem and the size of the real-world instances, it is not possible to use exact methods, and thus heuristics, meta-heuristics, or hyper-heuristics must be employed. The majority of heuristic approaches are based on iterative search, where the quality of intermediate solutions must be calculated. Unfortunately, this is computationally highly expensive because these problems have many constraints and some are very complex. In this study, we propose a machine learning technique as a tool to accelerate the evaluation phase in heuristic approaches. The solution is based on a simple classifier, which is able to determine whether the changed solution (more precisely, the changed part of the solution) is better than the original or not. This decision is made much faster than a standard cost-oriented evaluation process. However, the classification process cannot guarantee 100% correctness. Therefore, our approach, which is illustrated using a tabu search algorithm in this study, includes a filtering mechanism, where the classifier rejects the majority of the potentially bad solutions and the remaining solutions are then evaluated in a standard manner. We also show how the boosting algorithms can improve the quality of the final solution compared with a simple classifier. We verified our proposed approach and premises, based on standard and real-world benchmark instances, to demonstrate the significant speedup obtained with comparable solution quality.