Dispatching rule selection with Gaussian processes

Research output: Journal contributionsJournal articlesResearchpeer-review

Standard

Dispatching rule selection with Gaussian processes. / Heger, Jens; Hildebrandt, Torsten; Scholz-Reiter, Bernd.
In: Central European Journal of Operations Research, Vol. 23, No. 1, 03.2015, p. 235-249.

Research output: Journal contributionsJournal articlesResearchpeer-review

Harvard

APA

Vancouver

Heger J, Hildebrandt T, Scholz-Reiter B. Dispatching rule selection with Gaussian processes. Central European Journal of Operations Research. 2015 Mar;23(1):235-249. doi: 10.1007/s10100-013-0322-7

Bibtex

@article{016051ed68b2451ba5ca60a8cfb7f077,
title = "Dispatching rule selection with Gaussian processes",
abstract = "Decentralized scheduling with dispatching rules is applied in many fields of logistics and production, especially in highly complex and dynamic scenarios, such as semiconductor manufacturing. Many dispatching rules are proposed in the literature, which perform well on specific scenarios. No rule is known, however, consistently outperforming all other rules. One approach to meet this challenge and improve scheduling performance is to select and switch dispatching rules depending on current system conditions. For this task machine learning techniques (e.g., Artificial Neural Networks) are frequently used. In this paper we investigate the use of a machine learning technique not applied to this task before: Gaussian process regression. Our analysis shows that Gaussian processes predict dispatching rule performance better than Neural Networks in most settings. Additionally, already a single Gaussian Process model can easily provide a measure of prediction quality. This is in contrast to many other machine learning techniques. We show how to use this measure to dynamically add additional training data and incrementally improve the model where necessary. Results therefore suggest, Gaussian processes are a very promising technique, which can lead to better scheduling performance (e.g., reduced mean tardiness) compared to other techniques.",
keywords = "Dispatching rules, Gaussian processes, Machine learning, Planning and scheduling, Production management and logistics, Engineering",
author = "Jens Heger and Torsten Hildebrandt and Bernd Scholz-Reiter",
year = "2015",
month = mar,
doi = "10.1007/s10100-013-0322-7",
language = "English",
volume = "23",
pages = "235--249",
journal = "Central European Journal of Operations Research",
issn = "1435-246X",
publisher = "Springer",
number = "1",

}

RIS

TY - JOUR

T1 - Dispatching rule selection with Gaussian processes

AU - Heger, Jens

AU - Hildebrandt, Torsten

AU - Scholz-Reiter, Bernd

PY - 2015/3

Y1 - 2015/3

N2 - Decentralized scheduling with dispatching rules is applied in many fields of logistics and production, especially in highly complex and dynamic scenarios, such as semiconductor manufacturing. Many dispatching rules are proposed in the literature, which perform well on specific scenarios. No rule is known, however, consistently outperforming all other rules. One approach to meet this challenge and improve scheduling performance is to select and switch dispatching rules depending on current system conditions. For this task machine learning techniques (e.g., Artificial Neural Networks) are frequently used. In this paper we investigate the use of a machine learning technique not applied to this task before: Gaussian process regression. Our analysis shows that Gaussian processes predict dispatching rule performance better than Neural Networks in most settings. Additionally, already a single Gaussian Process model can easily provide a measure of prediction quality. This is in contrast to many other machine learning techniques. We show how to use this measure to dynamically add additional training data and incrementally improve the model where necessary. Results therefore suggest, Gaussian processes are a very promising technique, which can lead to better scheduling performance (e.g., reduced mean tardiness) compared to other techniques.

AB - Decentralized scheduling with dispatching rules is applied in many fields of logistics and production, especially in highly complex and dynamic scenarios, such as semiconductor manufacturing. Many dispatching rules are proposed in the literature, which perform well on specific scenarios. No rule is known, however, consistently outperforming all other rules. One approach to meet this challenge and improve scheduling performance is to select and switch dispatching rules depending on current system conditions. For this task machine learning techniques (e.g., Artificial Neural Networks) are frequently used. In this paper we investigate the use of a machine learning technique not applied to this task before: Gaussian process regression. Our analysis shows that Gaussian processes predict dispatching rule performance better than Neural Networks in most settings. Additionally, already a single Gaussian Process model can easily provide a measure of prediction quality. This is in contrast to many other machine learning techniques. We show how to use this measure to dynamically add additional training data and incrementally improve the model where necessary. Results therefore suggest, Gaussian processes are a very promising technique, which can lead to better scheduling performance (e.g., reduced mean tardiness) compared to other techniques.

KW - Dispatching rules

KW - Gaussian processes

KW - Machine learning

KW - Planning and scheduling

KW - Production management and logistics

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=84881500606&partnerID=8YFLogxK

U2 - 10.1007/s10100-013-0322-7

DO - 10.1007/s10100-013-0322-7

M3 - Journal articles

AN - SCOPUS:84881500606

VL - 23

SP - 235

EP - 249

JO - Central European Journal of Operations Research

JF - Central European Journal of Operations Research

SN - 1435-246X

IS - 1

ER -

Recently viewed

Publications

  1. On robustness properties in permanent magnet machine control by using decoupling controller
  2. Integrating the underlying structure of stochasticity into community ecology
  3. Globally asymptotic output feedback tracking of robot manipulators with actuator constraints
  4. Mathematical relation between extended connectivity and eigenvector coefficients.
  5. Should learners use their hands for learning? Results from an eye-tracking study
  6. »HOW TO MAKE YOUR OWN SAMPLES«
  7. Harvesting information from captions for weakly supervised semantic segmentation
  8. Fast, Fully Automated Analysis of Voriconazole from Serum by LC-LC-ESI-MS-MS with Parallel Column-Switching Technique
  9. Analysis And Comparison Of Dispatching RuleBased Scheduling In Dual-Resource Constrained Shop-Floor Scenarios
  10. Closed-form Solution for the Direct Kinematics Problem of the Planar 3-RPR Parallel Mechanism
  11. Exploration strategies, performance, and error consequences when learning a complex computer task
  12. Lessons learned for spatial modelling of ecosystem services in support of ecosystem accounting
  13. Construct Objectification and De-Objectification in Organization Theory
  14. Holistic and scalable ranking of RDF data
  15. Lyapunov Convergence Analysis for Asymptotic Tracking Using Forward and Backward Euler Approximation of Discrete Differential Equations
  16. Contextual movement models based on normalizing flows
  17. Global Finite-Time Stabilization of Planar Linear Systems With Actuator Saturation
  18. Analyzing User Journey Data In Digital Health: Predicting Dropout From A Digital CBT-I Intervention
  19. Web-scale extension of RDF knowledge bases from templated websites
  20. Clause identification using entropy guided transformation learning
  21. Experimentally established correlation of friction surfacing process temperature and deposit geometry
  22. Interpreting Strings, Weaving Threads
  23. Generating Energy Optimal Powertrain Force Trajectories with Dynamic Constraints
  24. Analyzing math teacher students' sensitivity for aspects of the complexity of problem oriented mathematics instruction
  25. FaST: A linear time stack trace alignment heuristic for crash report deduplication
  26. What does it mean to be sensitive for the complexity of (problem oriented) teaching?
  27. Improving students’ science text comprehension through metacognitive self-regulation when applying learning strategies
  28. A new way of assessing the interaction of a metallic phase precursor with a modified oxide support substrate as a source of information for predicting metal dispersion
  29. Computing regression statistics from grouped data
  30. Performance analysis for loss systems with many subscribers and concurrent services
  31. Stimulating Computing
  32. TARGET SETTING FOR OPERATIONAL PERFORMANCE IMPROVEMENTS - STUDY CASE -