A Boosting Algorithm for the Classifiers in the Nurse Rostering Problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00208850" target="_blank" >RIV/68407700:21230/13:00208850 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

A Boosting Algorithm for the Classifiers in the Nurse Rostering Problems

Popis výsledku v původním jazyce

Nurse rostering problem is a well-known combinatorial problem. It can be solved by many (meta/hyper) heuristics while these methods are based on two usual steps: generate new solutions first and then determine their quality using an objective function. Unfortunately, this process is very expensive in terms of the computational complexity. Thus, we propose a faster evaluation of the objective function based on the solution structure. The idea is to mimic the human mind because the human schedulers are able to quickly recognize an obviously bad roster using only their own experience instead of complex computing. For this purpose, a neural network as a classifier can be used not only to distinguish between good and bad solutions but also to determine howmuch good or bad the solutions are. We apply an adaptive boosting algorithm to achieve more precise classification rates too. The results from the experiments show that the proposed approaches can reduce the runtime of the scheduling algo

Název v anglickém jazyce

A Boosting Algorithm for the Classifiers in the Nurse Rostering Problems

Popis výsledku anglicky

Nurse rostering problem is a well-known combinatorial problem. It can be solved by many (meta/hyper) heuristics while these methods are based on two usual steps: generate new solutions first and then determine their quality using an objective function. Unfortunately, this process is very expensive in terms of the computational complexity. Thus, we propose a faster evaluation of the objective function based on the solution structure. The idea is to mimic the human mind because the human schedulers are able to quickly recognize an obviously bad roster using only their own experience instead of complex computing. For this purpose, a neural network as a classifier can be used not only to distinguish between good and bad solutions but also to determine howmuch good or bad the solutions are. We apply an adaptive boosting algorithm to achieve more precise classification rates too. The results from the experiments show that the proposed approaches can reduce the runtime of the scheduling algo

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/7H12008" target="_blank" >7H12008: Design, Monitoring and Operation of Adaptive Networked Embedded Systems</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

POSTER 2013 - 17th International Student Conference on Electrical Engineering

ISBN

978-80-01-05242-6

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

Czech Technical University

Místo vydání

Prague

Místo konání akce

Prague

Datum konání akce

16. 5. 2013

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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