Roster evaluation based on classifiers for the nurse rostering problem

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00302613" target="_blank" >RIV/68407700:21230/16:00302613 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/s10732-016-9314-9" target="_blank" >http://dx.doi.org/10.1007/s10732-016-9314-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10732-016-9314-9" target="_blank" >10.1007/s10732-016-9314-9</a>

Result language

angličtina

Original language name

Roster evaluation based on classifiers for the nurse rostering problem

Original language description

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.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JC - Computer hardware and software

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Heuristics

ISSN

1381-1231

e-ISSN

—

Volume of the periodical

22

Issue of the periodical within the volume

5

Country of publishing house

US - UNITED STATES

Number of pages

31

Pages from-to

667-697

UT code for WoS article

000386163700002

EID of the result in the Scopus database

2-s2.0-84986243920