Machine Learning for Dynamic Job Shop Scheduling Problem: Literature Review

Nawres Boussadia, Olfa Belkahla Driss

2021

Abstract

In the last ten years, Machine Learning (ML) techniques have taken a huge leap forward and researchers have started to consider ML for job scheduling problems in the industrial field, especially dynamic job shop scheduling. In this paper, we mainly focus on the dynamic scheduling problem, which is more complex and difficult to solve and we propose to regroup the methods and approaches used to face it. Therefore, we give a review of machine and deep learning methods applied to dynamic job shop scheduling problems. In this way, our work provides a resume of the concerned studies.

Paper Citation

in Harvard Style

Boussadia N. and Belkahla Driss O. (2021). Machine Learning for Dynamic Job Shop Scheduling Problem: Literature Review. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 444-450. DOI: 10.5220/0010736200003101

in Bibtex Style

@conference{bml21,
author={Nawres Boussadia and Olfa Belkahla Driss},
title={Machine Learning for Dynamic Job Shop Scheduling Problem: Literature Review},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={444-450},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010736200003101},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - Machine Learning for Dynamic Job Shop Scheduling Problem: Literature Review
SN - 978-989-758-559-3
AU - Boussadia N.
AU - Belkahla Driss O.
PY - 2021
SP - 444
EP - 450
DO - 10.5220/0010736200003101