Q-learning-based hyper-heuristic framework for estimating the energy consumption of electric buses for public transport

Author:

Turgut Oguz Emrah,Turgut Mert Sinan,Önçağ Ali Çaglar,Eliiyi Uğur,Eliiyi Deniz Türsel

Publisher

Springer Science and Business Media LLC

Reference72 articles.

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3. Fisher, H., Thompson, G.L.: Probabilistic learning combinations of local job-shop scheduling rules. In: Muth, J.F., Thompson, G.L. (eds.) Industrial Scheduling, pp. 225–251. Prentice-Hall, Hoboken (1963)

4. Denzinger, J., Fuchs, M., Fuchs, M.: High performance ATP systems by combining several AI methods. Technical Report, SEKI-Report SR-96-09, University of Kaiserslautern (1997).

5. Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Woodwar, J.R.: A classification of hyper-heuristic approaches. In: Michel, G., Jean-Yves, P. (eds.) Handbook of Metaheuristics, pp. 449–468. Springer, Berlin (2010)

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