Data-driven Algorithm for Scheduling with Total Tardiness

Michal Bouška, Antonín Novák, Přemysl Šůcha, István Módos, Zdeněk Hanzálek

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-art NBR heuristic.

Download


Paper Citation


in Harvard Style

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 - Volume 1: ICORES, ISBN 978-989-758-396-4, pages 59-68. DOI: 10.5220/0008915300590068


in Bibtex Style

@conference{icores20,
author={Michal Bouška and Antonín Novák and Přemysl 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 - Volume 1: ICORES,},
year={2020},
pages={59-68},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008915300590068},
isbn={978-989-758-396-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Data-driven Algorithm for Scheduling with Total Tardiness
SN - 978-989-758-396-4
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