Bibliographische Notiz

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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