| References: [1] Altendorfer, K., Felberbauer, T., Jodlbauer, H. (2016). Effects of forecast errors on optimal utilisation in aggregate production planning with stochastic customer demand. International Journal of Production Research, 54(12), 3718–3735 (2016)
[2] Bousqaoui, H., Achchab, S., Tikito, K. (2017). Machine learning applications in supply chains: long short-term memory for demand forecasting. In: International Conference of Cloud Computing Technologies and Applications. pp. 301–317. Springer (2017)
[3] Box, G. E., Jenkins, G.M., Reinsel, G. C., Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
[4] Cox, J. F., Blackstone, J. H. (2002). APICS dictionary. Amer Production & Inventory (2002)
[5] Disney, S. M., Farasyn, I., Lambrecht, M., Towill, D. R., Van de Velde, W. (2006). Taming the bullwhip effect whilst watching customer service in a single supply chain echelon. European Journal of Operational Research 173(1), 151–172 (2006)
[6] Graves, A. (2013). Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)
[7] Graves, A., Jaitly, N. (2014). Towards end-to-end speech recognition with recurrent neural networks. In: International conference on machine learning. p 1764–1772 (2014)
[8] Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., Schmidhuber, J. (2008). A novel connectionist system for unconstrained handwriting recognition. IEEE transactions on pattern analysis and machine intelligence, 31(5), 855–868 (2008)
[9] Graves, A., Mohamed, A.r., Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing. pp. 6645–6649. IEEE (2013)
[10] Harvey, A.C., Peters, S. (1990). Estimation procedures for structural time series models. Journal of Forecasting, 9(2), 89–108.
[11] Hochreiter, S., Schmidhuber, J. (1997). Long short-term memory. Neural computation 9(8), 1735–1780 (1997)
[12] Hopp, W. J., Spearman, M. L. (2011). Factory physics. Waveland Press (2011)
[13] Jacobs, F. R. (2011). Manufacturing planning and control for supply chain management. McGraw-Hill (2011)
[14] Kiros, R., Salakhutdinov, R., Zemel, R.S. (2014). Unifying visual-semantic embeddings with multimodal neural language models. arXiv preprint arXiv:1411.2539 (2014)
[15] Lee, T., Adam Jr, E.E. (1986). Forecasting error evaluation in material requirements planning (mrp) production-inventory systems. Management Science, 32(9), 1186–1205 (1986)
[16] Lee, T., Shih, W. (1989). Optimal forecast biasing in theoretical inventory models. The International Journal Of Production Research, 27(5), 809–830 (1989)
[17] Li, Q., Disney, S. M. (2017). Revisiting rescheduling: Mrp nervousness and the bullwhip effect. International Journal of Production Research, 55(7), 1992–2012 (2017)
[18] Mabert, V.A. (2007). The early road to material requirements planning. Journal of Operations Management 25(2), 346–356.
[19] Moreno, R. H., Garcia, O., et al. (2018). Model of neural networks for fertilizer recommendation and amendments in pasture crops. In: 2018 ICAI Workshops (ICAIW). p 1–5. IEEE (2018)
[20] Nahmias, S., Cheng, Y. (2005). Production and operations analysis, vol. 6. McGraw-hill New York (2005)
[21] Orlicki, J.A. (1975). Material requirements planning: the new way of life in production and inventory management. McGraw-Hill (1975)
[22] Priore, P., Ponte, B., Rosillo, R., de la Fuente, D. (2018). Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments. International Journal of Production Research, p 1–15 (2018)
[23] Ptak, C. A., Smith, C. (2016). Demand Driven Material Requirements Planning (DDMRP). Industrial Press, Incorporated (2016)
[24] Romero-Gelvez, J. I., Gonzales-Cogua, W. C., Herrera-Cuartas, J. A. (). Cvrptw model for cargo collection with heterogeneous capacity-fleet. In: International Conference on Applied Informatics. Springer (2019)
[25] Sridharan, V., BERRY, W.L. Master production scheduling make-to-stock products: a framework for analysis. The International Journal of Production Research, 28(3), 541–558.
[26] Taylor, S. J., Letham, B. Forecasting at scale. The American Statistician 72(1), 37–45.
[27] Vinyals, O., Kaiser, A., Koo, T., Petrov, S., Sutskever, I., Hinton, G. Grammar as a foreign language. In: Advances in neural information processing systems. p 2773–2781.
[28] Vinyals, O., Toshev, A., Bengio, S., Erhan, D. Show and tell: A neural image caption generator. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3156–3164.
[29] Voß, S., Woodruff, D. L. Introduction to computational optimization models for production planning in a supply chain, vol. 240. Springer Science & Business Media.
[30] Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., Bengio, Y. Show, attend and tell: Neural image caption generation with visual attention. arXiv preprint arXiv:1502.03044. |
