Data-driven Single Machine Scheduling Minimizing Weighted Number of Tardy Jobs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00370778" target="_blank" >RIV/68407700:21230/23:00370778 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/23:00370778

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-49008-8_38" target="_blank" >https://doi.org/10.1007/978-3-031-49008-8_38</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-49008-8_38" target="_blank" >10.1007/978-3-031-49008-8_38</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data-driven Single Machine Scheduling Minimizing Weighted Number of Tardy Jobs

Popis výsledku v původním jazyce

We tackle a single-machine scheduling problem where each job is characterized by weight, duration, due date, and deadline, while the objective is to minimize the weighted number of tardy jobs. The problem is strongly NP-hard and has practical applications in various domains, such as customer service and production planning. The best known exact approach uses a branch-and-bound structure, but its efficiency varies depending on the distribution of job parameters. To address this, we propose a new data-driven heuristic algorithm that considers the parameter distribution and uses machine learning and integer linear programming to improve the optimality gap. The algorithm also guarantees to obtain a feasible solution if it exists. Experimental results show that the proposed approach outperforms the current state-of-the-art heuristic. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Název v anglickém jazyce

Data-driven Single Machine Scheduling Minimizing Weighted Number of Tardy Jobs

Popis výsledku anglicky

We tackle a single-machine scheduling problem where each job is characterized by weight, duration, due date, and deadline, while the objective is to minimize the weighted number of tardy jobs. The problem is strongly NP-hard and has practical applications in various domains, such as customer service and production planning. The best known exact approach uses a branch-and-bound structure, but its efficiency varies depending on the distribution of job parameters. To address this, we propose a new data-driven heuristic algorithm that considers the parameter distribution and uses machine learning and integer linear programming to improve the optimality gap. The algorithm also guarantees to obtain a feasible solution if it exists. Experimental results show that the proposed approach outperforms the current state-of-the-art heuristic. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Progress in Artificial Intelligence, 22nd EPIA Conference on Artificial Intelligence, EPIA 2023, Faial Island, Azores, September 5–8, 2023, Proceedings, Part I

ISBN

978-3-031-49007-1

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

12

Strana od-do

483-494

Název nakladatele

Springer, Cham

Místo vydání

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Místo konání akce

Faial Island

Datum konání akce

5. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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