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). 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 -

Recently viewed

Publications

  1. Dimension estimates for certain sets of infinite complex continued fractions
  2. Explicit and Implicit Framing Effects on Product Attitudes When Using Country-of- Origin Cues
  3. Don’t underestimate the problems of user centredness in software development projectsthere are many!?
  4. The effects of different on-line adaptive response time limits on speed and amount of learning in computer assisted instruction and intelligent tutoring
  5. Control of a Three-Axis Robot with Super Twisting Sliding Mode Control
  6. A New Framework for Production Planning and Control to Support the Positioning in Fields of Tension Created by Opposing Logistic Objectives
  7. Detection time analysis of propulsion system fault effects in a hexacopter
  8. Reality-Based Tasks with Complex-Situations
  9. Introduction Mobile Digital Practices. Situating People, Things, and Data
  10. Mapping the intersection of planetary boundaries and environmentally extended input-output analysis: A systematic literature review
  11. The relationship between long-term memory ability and instructed second language learning
  12. Guest Editorial - ''Econometrics of Anonymized Micro Data''
  13. Experimental Evaluation of Data Fusion Techniques and Adaptive Control for Mobile Robot Localization
  14. Measuring cognitive load with subjective rating scales during problem solving
  15. Load mitigation and power tracking capability for wind turbines using linear matrix inequality-based control design
  16. Joint Item Response Models for Manual and Automatic Scores on Open-Ended Test Items
  17. Comparison of Odor Thresholds obtained by a Three Alternative Choice Procedure and by the Method of Limits
  18. Comparison of different FEM codes approach for extrusion process analysis
  19. Global Finite-Time Stabilization of Planar Linear Systems With Actuator Saturation
  20. Effectiveness of a guided multicomponent internet and mobile gratitude training program - A pragmatic randomized controlled trial
  21. A Note on Estimation of Empirical Models for Margins of Exports with Unknown Non-linear Functional Forms