Gaussian processes for dispatching rule selection in production scheduling: Comparison of learning techniques

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Standard

Gaussian processes for dispatching rule selection in production scheduling: Comparison of learning techniques. / Scholz-Reiter, B.; Heger, J.; Hildebrandt, T.
Proceedings - IEEE International Conference on Data Mining, ICDM. IEEE - Institute of Electrical and Electronics Engineers Inc., 2010. S. 631-638 (IEEE International Conference on Data Mining Workshops).

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Harvard

Scholz-Reiter, B, Heger, J & Hildebrandt, T 2010, Gaussian processes for dispatching rule selection in production scheduling: Comparison of learning techniques. in Proceedings - IEEE International Conference on Data Mining, ICDM. IEEE International Conference on Data Mining Workshops, IEEE - Institute of Electrical and Electronics Engineers Inc., S. 631-638, 10th IEEE International Conference on Data Mining Workshops - 2010, Sydney, Australien, 14.12.10. https://doi.org/10.1109/ICDMW.2010.19

APA

Scholz-Reiter, B., Heger, J., & Hildebrandt, T. (2010). Gaussian processes for dispatching rule selection in production scheduling: Comparison of learning techniques. In Proceedings - IEEE International Conference on Data Mining, ICDM (S. 631-638). (IEEE International Conference on Data Mining Workshops). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDMW.2010.19

Vancouver

Scholz-Reiter B, Heger J, Hildebrandt T. Gaussian processes for dispatching rule selection in production scheduling: Comparison of learning techniques. in Proceedings - IEEE International Conference on Data Mining, ICDM. IEEE - Institute of Electrical and Electronics Engineers Inc. 2010. S. 631-638. (IEEE International Conference on Data Mining Workshops). doi: 10.1109/ICDMW.2010.19

Bibtex

@inbook{b129054fbbc6430fa61df64ba1d8b42b,
title = "Gaussian processes for dispatching rule selection in production scheduling: Comparison of learning techniques",
abstract = "Decentralized scheduling with dispatching rules is applied in many fields of logistics and production, especially in semiconductor manufacturing, which is characterized by high complexity and dynamics. Many dispatching rules have been found, which perform well on different scenarios, however no rule has been found, which outperforms other rules across various objectives. To tackle this drawback, approaches, which select dispatching rules depending on the current system conditions, have been proposed. Most of these use learning techniques to switch between rules regarding the current system status. Since the study of Rasmussen [1] has shown that Gaussian processes as a machine learning technique have outperformed other techniques like neural networks under certain conditions, we propose to use them for the selection of dispatching rules in dynamic scenarios. Our analysis has shown that Gaussian processes perform very well in this field of application. Additionally, we showed that the prediction quality Gaussian processes provide could be used successfully. {\textcopyright} 2010 IEEE.",
keywords = "Engineering",
author = "B. Scholz-Reiter and J. Heger and T. Hildebrandt",
note = "Cited By :1 Export Date: 23 May 2016 References: Rasmussen, C.E., (1996) Evaluation of Gaussian Processes and Other Methods for Non-linear Regression, , PhD thesis, University of Toronto; Blackstone Jr. John, H., Phillips Don, T., Hogg Gary, L., STATE-OF-THE-ART SURVEY OF DISPATCHING RULES FOR MANUFACTURING JOB SHOP OPERATIONS. (1982) International Journal of Production Research, 20 (1), pp. 27-45; Haupt, R., A survey of priority rule-based scheduling (1989) OR Spektrum, 11 (1), pp. 3-16; Panwalkar, S.S., Iskander, W., A survey of scheduling rules (1977) Operations Research, 25 (1), pp. 45-61; Hildebrandt, T., Heger, J., Scholz-Reiter, B., Towards improved dispatching rules for complex shop floor scenarios - A genetic programming approach (2010) Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, , Portland, USA, (accepted paper, to appear); Rajendran, C., Holthaus, O., A comparative study of dispatching rules in dynamic flowshops and jobshops (1999) European Journal of Operational Research, 116 (1), pp. 156-170; Mouelhi-Chibani, W., Pierreval, H., Training a neural network to select dispatching rules in real time (2010) Computers & Industrial Engineering, 58 (2), pp. 249-256; Williams, C.K.I., Rasmussen, C.E., Gaussian processes for regression (1996) Advances in Neural Information Processing Systems, 8, pp. 514-520; Alpaydin, E., (2004) Introduction to Machine Learning (Adaptive Computation and Machine Learning Series), 14 (1). , The MIT Press; Kotsiantis, S.B., Supervised machine learning: A review of classification techniques (2007) Informatica (Ljubljana), 31 (3), pp. 249-268; Priore, P., De La Fuente, D., Gomez, A., Puente, J., A review of machine learning in dynamic scheduling of flexible manufacturing systems (2001) Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM, 15 (3), pp. 251-263. , DOI 10.1017/S0890060401153059; Wu, S.-Y.D., Wysk, R., An application of discreteevent simulation to on-line control and scheduling in flexible manufacturing (1989) International Journal of Production Research, 27 (9), pp. 1603-1623; Sun, Y.-L., Yih, Y., An intelligent controller for manufacturing cells (1996) International Journal of Production Research, 34 (8), pp. 2353-2373; El-Bouri, A., Shah, P., A neural network for dispatching rule selection in a job shop (2006) International Journal of Advanced Manufacturing Technology, 31 (3-4), pp. 342-349. , DOI 10.1007/s00170-005-0190-y; O'Hagan, A., Curve fitting and optimal design (1978) Journal of the Royal Statistical Society, 40 (1), pp. 1-42; Rasmussen, C.E., Williams, C.K.I., (2006) Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning), , The MIT Press; Neal, R.M., (1996) Bayesian Learning for Neural Networks (Lecture Notes in Statistics), , 1st ed. Springer; Conway, R.W., Priority dispatching and job lateness in a job shop (1965) Journal of Industrial Engineering, 16, pp. 228-237; Holthaus, O., Rajendran, C., Efficient jobshop dispatching rules: Further developments (2000) Production Planning and Control, 11 (2), pp. 171-178. , DOI 10.1080/095372800232379; Law, A.M., (2007) Simulation Modeling and Analysis, , 4th ed. Boston, USA: McGraw-Hill; Huffman, B.J., An object-oriented version of SIMLIB (a simple simulation package) (2001) INFORMS Transactions on Education, 2 (1), pp. 1-15; Williams, C., (2006) Gaussian Processes for Machine Learning - Software Examples, , http://www.gaussianprocess.org/gpml/code/matlab/doc; Bonilla, E.V., Ming, K., Chai, A., Williams, C.K.I., Multi-task gaussian process prediction (2008) Advances in Neural Information Processing Systems, 20; Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H., The weka data mining software: An update (2009) SIGKDD Explor. Newsl., 11 (1), pp. 10-18; Cleary, J.G., Trigg, L.E., K*: An instance-based learner using an entropic distance measure (1995) 12th International Conference on Machine Learning, pp. 108-114. , Morgan Kaufmann, San Francisco; 10th IEEE International Conference on Data Mining Workshops - 2010, 10th IEEE ICDM - 2010 ; Conference date: 14-12-2010 Through 17-12-2010",
year = "2010",
doi = "10.1109/ICDMW.2010.19",
language = "English",
isbn = "978-1-4244-9244-2",
series = "IEEE International Conference on Data Mining Workshops",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",
pages = "631--638",
booktitle = "Proceedings - IEEE International Conference on Data Mining, ICDM",
address = "United States",
url = "http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=7127",

}

RIS

TY - CHAP

T1 - Gaussian processes for dispatching rule selection in production scheduling

T2 - 10th IEEE International Conference on Data Mining Workshops - 2010

AU - Scholz-Reiter, B.

AU - Heger, J.

AU - Hildebrandt, T.

N1 - Conference code: 10

PY - 2010

Y1 - 2010

N2 - Decentralized scheduling with dispatching rules is applied in many fields of logistics and production, especially in semiconductor manufacturing, which is characterized by high complexity and dynamics. Many dispatching rules have been found, which perform well on different scenarios, however no rule has been found, which outperforms other rules across various objectives. To tackle this drawback, approaches, which select dispatching rules depending on the current system conditions, have been proposed. Most of these use learning techniques to switch between rules regarding the current system status. Since the study of Rasmussen [1] has shown that Gaussian processes as a machine learning technique have outperformed other techniques like neural networks under certain conditions, we propose to use them for the selection of dispatching rules in dynamic scenarios. Our analysis has shown that Gaussian processes perform very well in this field of application. Additionally, we showed that the prediction quality Gaussian processes provide could be used successfully. © 2010 IEEE.

AB - Decentralized scheduling with dispatching rules is applied in many fields of logistics and production, especially in semiconductor manufacturing, which is characterized by high complexity and dynamics. Many dispatching rules have been found, which perform well on different scenarios, however no rule has been found, which outperforms other rules across various objectives. To tackle this drawback, approaches, which select dispatching rules depending on the current system conditions, have been proposed. Most of these use learning techniques to switch between rules regarding the current system status. Since the study of Rasmussen [1] has shown that Gaussian processes as a machine learning technique have outperformed other techniques like neural networks under certain conditions, we propose to use them for the selection of dispatching rules in dynamic scenarios. Our analysis has shown that Gaussian processes perform very well in this field of application. Additionally, we showed that the prediction quality Gaussian processes provide could be used successfully. © 2010 IEEE.

KW - Engineering

U2 - 10.1109/ICDMW.2010.19

DO - 10.1109/ICDMW.2010.19

M3 - Article in conference proceedings

SN - 978-1-4244-9244-2

T3 - IEEE International Conference on Data Mining Workshops

SP - 631

EP - 638

BT - Proceedings - IEEE International Conference on Data Mining, ICDM

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

Y2 - 14 December 2010 through 17 December 2010

ER -

DOI

Zuletzt angesehen

Aktivitäten

  1. Project Workshop on "Worker Flows, Match Quality, and Productivity" - 2019
  2. 13th IFAC and IEEE Conference on Programmable Devices and Embedded Systems - PDeS 2015
  3. Applied Econometrics with Stata for PhD Students
  4. Princeton University
  5. Thinking of Time - A Resource which Should be Allocated Equally
  6. Changing learning environments at university? Comparing the learning strategies of non-traditional European students engaged in lifelong learning.
  7. Predicting negotiation success with a multitude of negotiators’ inter-individual differences—a latent personality model of the successful negotiator
  8. Visualizing and analyzing big data sets: Results from the Student Bodies-Eating Disorders study
  9. "Crowds and Party" Reading Workshop with Jody Dean - 2019
  10. European University Institute
  11. Where is language use in the description of the Englishes? - ESSE 2006
  12. Comfort and Adaptive Cruise Control in Highly Automated Vehicles
  13. Panel Cointegration Testing with Time Trend and Analysis of Money Demand in OECD Countries
  14. Navigating Educational Ambidexterity: Exploring Leader-Member Exchange and Open Innovation
  15. Digital Capitalism meets Leberkaspeppi: Temporal Orientations in Business Models as a Source of Platform Power in Mature Industries
  16. THE EUROPEAN CONFERENCE ON MACHINE LEARNING & PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES 2017
  17. Ghosting the City – Zooming in on Otherwise Publics in Virtual Worlds
  18. 6th Workshop on Unintended Consequences
  19. Internet-based guided self-help to reduce depressive symptoms in teachers: Results from a randomized controlled trial
  20. Modelling biodegradability based on OECD 301D data for the design of mineralising ionic liquids
  21. Mathematical and Computational Applications (Fachzeitschrift)
  22. Response to Philip Hogh: Suffering, Pain and the Idea of Progress

Publikationen

  1. Cue predictability changes scaling in eye-movement fluctuations
  2. Service Level Driven Stock Allocation
  3. Digging into the roots
  4. Design for Product Care—Development of Design Strategies and a Toolkit for Sustainable Consumer Behaviour
  5. The effect of structural complexity on large mammal occurrence in revegetation
  6. Towards Advanced Learning in Dispatching Rule-Based Scheuling
  7. Integrating inductive and deductive analysis to identify and characterize archetypical social-ecological systems and their changes
  8. "Introduction," communication +1
  9. What Makes for a Good Theory? How to Evaluate a Theory Using the Strength Model of Self-Control as an Example
  10. Embedding Evidence on Conservation Interventions Within a Context of Multilevel Governance
  11. Drafts in Action
  12. Development and comparison of processing maps of Mg-3Sn-1Ca alloy from data obtained in tension versus compression
  13. Modeling of temperature- and strain-driven intermetallic compound evolution in an Al-Mg system via a multiphase-field approach with application to refill friction stir spot welding
  14. Discriminative clustering for market segmentation
  15. From Open Access to Open Science
  16. How to move the transition to sustainable food consumption towards a societal tipping point
  17. Integrating business models and enterprise architecture
  18. Concepts, Formats, and Methods of Participation
  19. Effective digital practice in the competence-oriented English as a foreign language classroom in Germany
  20. Trajectory tracking using MPC and a velocity observer for flat actuator systems in automotive applications