Abstract
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.
This work was supported by the Czech MEYS under the ERC CZ project POSTMAN no. LL1902, by the Grant Agency of the Czech Technical University in Prague, grant No. SGS22/167/OHK3/3T/13 and by the Grant Agency of the Czech Republic under the Project GACR 22-31670S. This article is part of the RICAIP project that has received funding from the EU’s Horizon 2020 research and innovation programme under grant agreement No 857306.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baptiste, P., Croce, F.D., Grosso, A., T’kindt, V.: Sequencing a single machine with due dates and deadlines: an ILP-based approach to solve very large instances. J. Sched. 13(1), 39–47 (2010)
Bengio, Y., Lodi, A., Prouvost, A.: Machine learning for combinatorial optimization: a methodological tour d’horizon. Eur. J. Oper. Res. 290(2), 405–421 (2021)
Bouška, M., Šcha, P., Novák, A., Hanzálek, Z.: Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness. Eur. J. Oper. Res. (2022)
Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., Smola, A.J.: AutoGluon-tabular: robust and accurate AutoML for structured data. CoRR arXiv:abs/2003.06505 (2020)
Graham, R., Lawler, E., Lenstra, J., Kan, A.: Optimization and approximation in deterministic sequencing and scheduling: a survey. In: Hammer, P., Johnson, E., Korte, B. (eds.) Discrete Optimization II, Annals of Discrete Mathematics, vol. 5, pp. 287–326. Elsevier (1979)
Hariri, A.M.A., Potts, C.N.: Single machine scheduling with deadlines to minimize the weighted number of tardy jobs. Manage. Sci. 40(12), 1712–1719 (1994)
Hejl, L., Šůcha, P., Novák, A., Hanzálek, Z.: Minimizing the weighted number of tardy jobs on a single machine: Strongly correlated instances. Eur. J. Oper. Res. 298(2), 413–424 (2022)
Karimi-Mamaghan, M., Mohammadi, M., Meyer, P., Karimi-Mamaghan, A.M., Talbi, E.G.: Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: a state-of-the-art. Eur. J. Oper. Res. 296(2), 393–422 (2022)
Li, Y., Fadda, E., Manerba, D., Tadei, R., Terzo, O.: Reinforcement learning algorithms for online single-machine scheduling. In: 2020 15th Conference on Computer Science and Information Systems (FedCSIS), pp. 277–283 (2020)
Mazyavkina, N., Sviridov, S., Ivanov, S., Burnaev, E.: Reinforcement learning for combinatorial optimization: a survey. Comput. Oper. Res. 134 (2021)
Parmentier, A., T’Kindt, V.: Structured learning based heuristics to solve the single machine scheduling problem with release times and sum of completion times. Eur. J. Oper. Res. (2022)
Pinedo, M.L.: Scheduling. Theory, Algorithms, and Systems, p. 233. Springer, New York (2012)
Yuan, J.: Unary NP-hardness of minimizing the number of tardy jobs with deadlines. J. Sched. 20(2), 211–218 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Antonov, N., Šucha, P., Janota, M. (2023). Data-driven Single Machine Scheduling Minimizing Weighted Number of Tardy Jobs. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14115. Springer, Cham. https://doi.org/10.1007/978-3-031-49008-8_38
Download citation
DOI: https://doi.org/10.1007/978-3-031-49008-8_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49007-1
Online ISBN: 978-3-031-49008-8
eBook Packages: Computer ScienceComputer Science (R0)