Dynamic Lot Size Optimization with Reinforcement Learning

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-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/worksArticle in conference proceedingsResearchpeer-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). https://doi.org/10.1007/978-3-031-05359-7_30

Bibtex

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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",
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 = "Germany",
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

Y1 - 2022

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

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 -