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Dynamic Lot Size Optimization with Reinforcement Learning

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Standard

Dynamic Lot Size Optimization with Reinforcement Learning. / Voss, Thomas; Bode, Christopher; Heger, Jens.
Dynamics in Logistics : Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany. ed. / Michael Freitag; Aseem Kinra; Hebert Kotzab; Nicole Megow. Cham: Springer Science and Business Media B.V., 2022. p. 376-385 (Lecture Notes in Logistics).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Harvard

Voss, T, Bode, C & Heger, J 2022, Dynamic Lot Size Optimization with Reinforcement Learning. in M Freitag, A Kinra, H Kotzab & N Megow (eds), Dynamics in Logistics : Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany. Lecture Notes in Logistics, Springer Science and Business Media B.V., Cham, pp. 376-385, International Conference on Dynamics in Logistics - LDIC 2022, Bremen, Bremen, Germany, 23.02.22. https://doi.org/10.1007/978-3-031-05359-7_30

APA

Voss, T., Bode, C., & Heger, J. (2022). Dynamic Lot Size Optimization with Reinforcement Learning. In M. Freitag, A. Kinra, H. Kotzab, & N. Megow (Eds.), Dynamics in Logistics : Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany (pp. 376-385). (Lecture Notes in Logistics). Springer Science and Business Media B.V.. https://doi.org/10.1007/978-3-031-05359-7_30

Vancouver

Voss T, Bode C, Heger J. Dynamic Lot Size Optimization with Reinforcement Learning. In Freitag M, Kinra A, Kotzab H, Megow N, editors, Dynamics in Logistics : Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany. Cham: Springer Science and Business Media B.V. 2022. p. 376-385. (Lecture Notes in Logistics). doi: 10.1007/978-3-031-05359-7_30

Bibtex

@inbook{3bb8beeaa08b41b7afe6cb00cd269e1a,
title = "Dynamic Lot Size Optimization with Reinforcement Learning",
abstract = "Production planning and control has a great influence on the economic efficiency and logistical performance of a company. In this context, this article gives an insight into the use of simulation as a virtual model of a filling machine in the process industry. Furthermore, it shows the possibilities of a reinforcement learning (RL) approach for dynamic lot sizing. The contribution indicates a possible implementation in an ERP system and shows how a decision support tool can support the planner to save up to 5% of costs compared to a human planner and a heuristic approach proposed by Groff.",
keywords = "Lot sizing, Reinforcement learning, Simulation, Engineering",
author = "Thomas Voss and Christopher Bode and Jens Heger",
year = "2022",
month = jan,
day = "1",
doi = "10.1007/978-3-031-05359-7_30",
language = "English",
isbn = "978-3-031-05358-0",
series = "Lecture Notes in Logistics",
publisher = "Springer Science and Business Media B.V.",
pages = "376--385",
editor = "Michael Freitag and Aseem Kinra and Hebert Kotzab and Nicole Megow",
booktitle = "Dynamics in Logistics",
address = "Netherlands",
note = "International Conference on Dynamics in Logistics - LDIC 2022, LDIC 2022 ; Conference date: 23-02-2022 Through 25-02-2022",
url = "https://www.ldic-conference.org/about-ldic",

}

RIS

TY - CHAP

T1 - Dynamic Lot Size Optimization with Reinforcement Learning

AU - Voss, Thomas

AU - Bode, Christopher

AU - Heger, Jens

N1 - Conference code: 8

PY - 2022/1/1

Y1 - 2022/1/1

N2 - Production planning and control has a great influence on the economic efficiency and logistical performance of a company. In this context, this article gives an insight into the use of simulation as a virtual model of a filling machine in the process industry. Furthermore, it shows the possibilities of a reinforcement learning (RL) approach for dynamic lot sizing. The contribution indicates a possible implementation in an ERP system and shows how a decision support tool can support the planner to save up to 5% of costs compared to a human planner and a heuristic approach proposed by Groff.

AB - Production planning and control has a great influence on the economic efficiency and logistical performance of a company. In this context, this article gives an insight into the use of simulation as a virtual model of a filling machine in the process industry. Furthermore, it shows the possibilities of a reinforcement learning (RL) approach for dynamic lot sizing. The contribution indicates a possible implementation in an ERP system and shows how a decision support tool can support the planner to save up to 5% of costs compared to a human planner and a heuristic approach proposed by Groff.

KW - Lot sizing

KW - Reinforcement learning

KW - Simulation

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85129725752&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/30271c6b-5ace-3c03-96a5-2bf273c9b26a/

U2 - 10.1007/978-3-031-05359-7_30

DO - 10.1007/978-3-031-05359-7_30

M3 - Article in conference proceedings

AN - SCOPUS:85129725752

SN - 978-3-031-05358-0

T3 - Lecture Notes in Logistics

SP - 376

EP - 385

BT - Dynamics in Logistics

A2 - Freitag, Michael

A2 - Kinra, Aseem

A2 - Kotzab, Hebert

A2 - Megow, Nicole

PB - Springer Science and Business Media B.V.

CY - Cham

T2 - International Conference on Dynamics in Logistics - LDIC 2022

Y2 - 23 February 2022 through 25 February 2022

ER -

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Enhancing SME Production Efficiency: A Case Study on Autonomous Manufacturing Systems

Prüfer, O. C. & Heger, J., 2025, In: Procedia CIRP. 134, p. 313-318 6 p.

Research output: Journal contributions › Conference article in journal › Research › peer-review

Manufacturing of irregular shapes through force control in incremental sheet forming with active medium

Thiery, S., Zein El Abdine, M., Heger, J. & Ben Khalifa, N., 01.05.2025, In: Advances in Industrial and Manufacturing Engineering. 10, 11 p., 100157.

Research output: Journal contributions › Journal articles › Research › peer-review

Rethink Textile Production - Developing sustainable concepts for textile industry using production simulation

Müller, K., Guerne, M. G., Block, B.-M. & Heger, J., 04.2025, 14th International Materials Education Symposium: Clare College, University of Cambridge Cambridge, UK April 10-11, 2025. Canonsburg: University of Cambridge, p. 27 1 p. ( Materials Education Symposium).

Research output: Contributions to collected editions/works › Published abstract in conference proceedings › Transfer › peer-review

Towards Fashion Renting: Identification of Influencing Factors for Consumer Behavior

Quadras, D., Müller, K., Pirola, F., Heger, J., Fani, V. & Bandinelli, R., 06.2025, Proceedings of the 39th ECMS International Conference on Modelling and Simulation, ECMS 2025: ECMS 2025, June 24th – June 27th, 2025, Catania, Italy. Scarpa, M., Cavalieri, S., Serrano, S. & De Vita, F. (eds.). Caserta: European Council for Modelling and Simulation, p. 62-68 7 p. (Communications of the ECMS; vol. 39, no. 1).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Beschleunigte Erstellung von Simulationsmodellen für Produktions- und Logistikprozesse mithilfe von GPT-basierten Large Language Models

Krämer, R. & Heger, J., 01.09.2024, ASIM SST 2024 Tagungsband Kurzbeiträge: 27. ASIM Symposium Simulationstechnik, 4.9.-6.9.2024, Universität der Bundeswehr München. Rose, O. & Uhlig, T. (eds.). Wien: ARGESIM Verlag , p. 21 - 24 4 p. (ARGESIM Report; vol. 46)(ASIM Mitteilung; vol. 189).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

DOI

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