The ALNS metaheuristic for the maintenance scheduling problem

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00351317" target="_blank" >RIV/68407700:21230/21:00351317 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/21:00351317

Výsledek na webu

<a href="https://doi.org/10.5220/0010552101560164" target="_blank" >https://doi.org/10.5220/0010552101560164</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0010552101560164" target="_blank" >10.5220/0010552101560164</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The ALNS metaheuristic for the maintenance scheduling problem

Popis výsledku v původním jazyce

Transmission maintenance scheduling (TMS) is an important optimization problem in the electricity distribution industry, with numerous variants studied and methods proposed over the last three decades. The ROADEF challenge 2020 addresses a novel version of the TMS problem, which stands out by having multiple time-dependent properties, constraints, and a risk-based aggregate objective function. Therefore, the problem is more complex than the previous formulations, and the existing methods are not directly applicable. This paper presents a method based on the Adaptive Large Neighborhood Search metaheuristic. The method is compared with the best-known solutions from the challenge qualification phase, in which more than 70 teams participated. The result shows that the method yields consistent performance over the whole dataset, as the method finds the best-known solutions for half of the dataset and finds solutions consistently within 0.5% gap.

Název v anglickém jazyce

The ALNS metaheuristic for the maintenance scheduling problem

Popis výsledku anglicky

Transmission maintenance scheduling (TMS) is an important optimization problem in the electricity distribution industry, with numerous variants studied and methods proposed over the last three decades. The ROADEF challenge 2020 addresses a novel version of the TMS problem, which stands out by having multiple time-dependent properties, constraints, and a risk-based aggregate objective function. Therefore, the problem is more complex than the previous formulations, and the existing methods are not directly applicable. This paper presents a method based on the Adaptive Large Neighborhood Search metaheuristic. The method is compared with the best-known solutions from the challenge qualification phase, in which more than 70 teams participated. The result shows that the method yields consistent performance over the whole dataset, as the method finds the best-known solutions for half of the dataset and finds solutions consistently within 0.5% gap.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotika pro Průmysl 4.0</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

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO

ISBN

978-989-758-522-7

ISSN

—

e-ISSN

2184-2809

Počet stran výsledku

9

Strana od-do

156-164

Název nakladatele

SciTePress

Místo vydání

Setùbal

Místo konání akce

online streaming

Datum konání akce

6. 7. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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