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Authors: Michal Bouška 1 ; 2 ; Antonín Novák 1 ; 2 ; Přemysl Šůcha 1 ; István Módos 1 ; 2 and Zdeněk Hanzálek 1

Affiliations: 1 Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslávských partyzánů 1580/3, Prague, Czech Republic ; 2 Czech Technical University in Prague, Faculty of Electrical Engineering, Department of Control Engineering, Karlovo náměstí 13, Prague, Czech Republic

Keyword(s): Single Machine Scheduling, Total Tardiness, Data-driven Method, Deep Neural Networks.

Abstract: In this paper, we investigate the use of deep learning for solving a classical N P-hard single machine scheduling problem where the criterion is to minimize the total tardiness. Instead of designing an end-to-end machine learning model, we utilize well known decomposition of the problem and we enhance it with a data-driven approach. We have designed a regressor containing a deep neural network that learns and predicts the criterion of a given set of jobs. The network acts as a polynomial-time estimator of the criterion that is used in a singlepass scheduling algorithm based on Lawler's decomposition theorem. Essentially, the regressor guides the algorithm to select the best position for each job. The experimental results show that our data-driven approach can efficiently generalize information from the training phase to significantly larger instances (up to 350 jobs) where it achieves an optimality gap of about 0.5%, which is four times less than the gap of the state-of-the-a rt NBR heuristic. (More)

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Paper citation in several formats:
Bouška, M.; Novák, A.; Šůcha, P.; Módos, I. and Hanzálek, Z. (2020). Data-driven Algorithm for Scheduling with Total Tardiness. In Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - ICORES; ISBN 978-989-758-396-4; ISSN 2184-4372, SciTePress, pages 59-68. DOI: 10.5220/0008915300590068

@conference{icores20,
author={Michal Bouška. and Antonín Novák. and P\v{r}emysl Šůcha. and István Módos. and Zdeněk Hanzálek.},
title={Data-driven Algorithm for Scheduling with Total Tardiness},
booktitle={Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - ICORES},
year={2020},
pages={59-68},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008915300590068},
isbn={978-989-758-396-4},
issn={2184-4372},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - ICORES
TI - Data-driven Algorithm for Scheduling with Total Tardiness
SN - 978-989-758-396-4
IS - 2184-4372
AU - Bouška, M.
AU - Novák, A.
AU - Šůcha, P.
AU - Módos, I.
AU - Hanzálek, Z.
PY - 2020
SP - 59
EP - 68
DO - 10.5220/0008915300590068
PB - SciTePress

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