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

Result code in 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>

Alternative codes found

RIV/68407700:21730/23:00370778

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

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

2023

Confidentiality

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

Article name in the collection

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

Number of pages

12

Pages from-to

483-494

Publisher name

Springer, Cham

Place of publication

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Event location

Faial Island

Event date

Sep 5, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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