Metamodelling of Inventory-Control Simulations Based on a Multilayer Perceptron

1. Bellman, R. (1957) Dynamic Programming. Princeton, Princeton University Press.Search in Google Scholar

2. Blanning, R.W. (1975) The construction and implementation of metamodels, Simulation, 24, 177–184. DOI:10.1177/003754977502400606.10.1177/003754977502400606Open DOISearch in Google Scholar

3. Buffa, E.S. and Taubert, W.H. (1972) Production-inventory systems planning and control (NTIS No. 658.4032 B8).Search in Google Scholar

4. Byrne, M.D. (2013) How many times should a stochastic model be run? An approach based on confidence intervals. In: Proceedings of the 12th International conference on cognitive modelling, Ottawa, July 2013.Search in Google Scholar

5. Cawley, G.C. and Talbot, N.L. (2010) On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research, 11(Jul), 2079–2107.Search in Google Scholar

6. Cybenko, G. (1989) Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2(4), 303–314.10.1007/BF02551274Search in Google Scholar

7. Domschke, W., Drexl, A., Klein, R. and Scholl, A. (2015) Einführung in Operations Research. 9th Ed., Berlin, Heidelberg: Springer Gabler.10.1007/978-3-662-48216-2Search in Google Scholar

8. Duan, Q. and Liao, T.W. (2013) Optimization of replenishment policies for decentralized and centralized capacitated supply chains under various demands. International Journal of Production Economics, 194–204.10.1016/j.ijpe.2012.11.004Search in Google Scholar

9. DynamicAction and IHL-group, (2015) Research Study: Retailers and the Ghost Economy $1.75 Trillion Reasons to be Afraid.Search in Google Scholar

10. Farhat, J. and Owayjan, M. (2017) ERP Neural Network Inventory Control. Procedia computer science, 114, 288-295.10.1016/j.procs.2017.09.039Search in Google Scholar

11. Glorot, X., Bordes, A. and Bengio, Y. (2011) Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, Lauderdale, FL, USA, June 2011, pp. 315-323.Search in Google Scholar

12. Haykin, S.S. (2009) Neural networks and learning machines. Pearson (ISBN-10: 0131471392).Search in Google Scholar

13. Hochmuth, C.A. and Kochel, P. (2012) How to order and transship in multi-location inventory systems: The simulation optimization approach. International Journal of Production Economics, 140, 646–654.10.1016/j.ijpe.2011.09.021Search in Google Scholar

14. Hornik, K. (1991) Approximation capabilities of multilayer feedforward networks. Neural networks, 4(2), 251–257.10.1016/0893-6080(91)90009-TSearch in Google Scholar

15. Hurlimann, T. (2007) Index notation in mathematics and modelling language LPL: theory and exercises. Department of Informatics University of Fribourg.Search in Google Scholar

16. IHL-group and Buzek G. (2015) Research Study: We Lost Australia! Retail’s $1.1 Trillion Inventory Distortion Problem.Search in Google Scholar

17. Iverson, K.E. (1962) A programming language. In: Proceedings of the spring joint computer conference. ACM, May 3, 1962, pp. 345-351.10.1145/1460833.1460872Search in Google Scholar

18. Jackson, I. (2019) GitHub repository “metainventory” - https://github.com/Jackil1993/metainventory, last accessed 2019/04/05.Search in Google Scholar

19. Jackson, I. and Tolujevs, J. (2019) The Discrete-Event Approach to Simulate Stochastic Multi-Product (Q, r) Inventory Control Systems. Information Modelling and Knowledge Bases XXX, 312, 32–39.Search in Google Scholar

20. Jackson, I., Tolujevs, J. and Reggelin, T. (2018) The Combination of Discrete-Event Simulation and Genetic Algorithm for Solving the Stochastic Multi-Product Inventory Optimization Problem. Transport and Telecommunication Journal, 19(3), 233–243.10.2478/ttj-2018-0020Search in Google Scholar

21. Jad, F. and Owayjan, M. (2017) ERP Neural Network Inventory Control. Procedia Computer Science, 114, 288–295.10.1016/j.procs.2017.09.039Search in Google Scholar

22. Jalali, H. and Nieuwenhuyse, I.V. (2015) Simulation optimization in inventory replenishment: a classification. IIE Transactions, 47(11), 1217–1235.10.1080/0740817X.2015.1019162Search in Google Scholar

23. Kingma, D.P. and Ba, J. (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.Search in Google Scholar

24. Law, A.M. and Kelton, W.D. (2000) Simulation modelling and analysis. New York: McGraw-Hill.Search in Google Scholar

25. Lechevalier, D., Hudak, S., Ak, R., Lee, Y.T., and Foufou, S. (2015) A neural network meta-model and its application for manufacturing. In: Proceedings of the IEEE International Conference on Big Data, Santa Clara, USA, 2015.10.1109/BigData.2015.7363903Search in Google Scholar

26. Lin, Y., Shie, J. and Tsai, C. (2009) Using an artificial neural network prediction model to optimize work-in-process inventory level for wafer fabrication. Expert Systems with Applications 36(2) 3421–3427.10.1016/j.eswa.2008.02.009Search in Google Scholar

27. Merkuryeva, G. (2004) Metamodelling for simulation applications in production and logistics. In: Proceedings of the Sim-Serv Workshop: Roadmap of simulation in manufacturing and logistics, pp. 1-6.Search in Google Scholar

28. Prestwich, S.D., Tarim, S.A., Rossi, R. and Hnich, B. (2012) A neuroevolutionary approach to stochastic inventory control in multi-echelon systems. International Journal of Production Research, 50, 2150–2160.10.1080/00207543.2011.574503Search in Google Scholar

29. Razali, N.M. and Wah, Y.B. (2011) Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests. Journal of statistical modelling and analytics, 2(1), 21–33.Search in Google Scholar

30. Tolujev, J., Lorenz, P., Beier, D. and Schriber, T.J. (1998) Assessment of simulation models based on trace-file analysis: a metamodeling approach. In: Proceedings of the Winter Simulation Conference. IEEE. December 1998, pp. 443-450.10.1109/WSC.1998.745020Search in Google Scholar

31. Tsai, S.C. and Zheng, Y.X. (2013) A simulation optimization approach for a two-echelon inventory system with service level constraints. European Journal of Operational Research, 229, 364–374.10.1016/j.ejor.2013.03.010Search in Google Scholar

32. Winskel, G. (2010) Set theory for computer science. Unpublished lecture notes.Search in Google Scholar