Dynamically changing sequencing rules with reinforcement learning in a job shop system with stochastic influences

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

Sequencing operations can be difficult, especially under uncertain conditions. Applying decentral sequencing rules has been a viable option; however, no rule exists that can outperform all other rules under varying system performance. For this reason, reinforcement learning (RL) is used as a hyper heuristic to select a sequencing rule based on the system status. Based on multiple training scenarios considering stochastic influences, such as varying inter arrival time or customers changing the product mix, the advantages of RL are presented. For evaluation, the trained agents are exploited in a generic manufacturing system. The best agent trained is able to dynamically adjust sequencing rules based on system performance, thereby matching and outperforming the presumed best static sequencing rules by ~ 3%. Using the trained policy in an unknown scenario, the RL heuristic is still able to change the sequencing rule according to the system status, thereby providing a robust performance.
Titel in ÜbersetzungDynamische Auswahl von Reihenfolgeregeln mit bestärkendem Lernen in einer Werkstattfertigung mit stochastischen Einflüssen
OriginalspracheEnglisch
TitelProceedings of the 2020 Winter Simulation Conference, WSC 2020
HerausgeberK.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, R. Thiesing
Anzahl der Seiten11
VerlagIEEE - Institute of Electrical and Electronics Engineers Inc.
Erscheinungsdatum14.12.2020
Seiten1608 - 1618
Aufsatznummer9383903
ISBN (elektronisch)978-1-7281-9499-8
DOIs
PublikationsstatusErschienen - 14.12.2020
VeranstaltungWinter Simulation Conference 2020: Simulation Drives Innovation - Orlando, USA / Vereinigte Staaten
Dauer: 14.12.202018.12.2020
http://meetings2.informs.org/wordpress/wsc2020/

Zugehörige Aktivitäten

DOI

Zuletzt angesehen

Publikationen

  1. Lyapunov stability analysis to set up a PI controller for a mass flow system in case of a non-saturating input
  2. Influence of Process Parameters and Die Design on the Microstructure and Texture Development of Direct Extruded Magnesium Flat Products
  3. Use of Machine-Learning Algorithms Based on Text, Audio and Video Data in the Prediction of Anxiety and Post-Traumatic Stress in General and Clinical Populations
  4. Combining a PI Controller with an Adaptive Feedforward Control in PMSM
  5. “Ideation is Fine, but Execution is Key”
  6. A Multilevel Inverter Bridge Control Structure with Energy Storage Using Model Predictive Control for Flat Systems
  7. Sliding mode and model predictive control for inverse pendulum
  8. Model predictive control for switching gain adaptation in a sliding mode controller of a DC drive with nonlinear friction
  9. Restoring Causal Analysis to Structural Equation ModelingReview of Causality: Models, Reasoning, and Inference (2nd Edition), by Judea Pearl
  10. Effects of diversity versus segregation on automatic approach and avoidance behavior towards own and other ethnic groups
  11. Understanding Low-Code Evolution, Adoption and Ecosystem for Software Development
  12. An observer for sensorless variable valve control in camless internal combustion engines
  13. The Low-Code Phenomenon: Mapping the Intellectual Structure of Research
  14. Evaluating a Bayesian Student Model of Decimal Misconceptions
  15. Operational integration of EMIS and ERP systems
  16. The identification of up-And downstream industries using input-output tables and a firm-level application to minority shareholdings
  17. Concepts
  18. Moving Towards Measuring Multifunctionality in Ecosystems: FieldScreen – A Mobile Positioning System for Non-Invasive Measurement of Plant Traits in Field Experiments
  19. Document assignment in multi-site search engines
  20. German Utilities and distributed PV