@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",