DLINE Journals portal

[1] Inc, G., Top 10 Strategic Technology Trends for 2019, ed, 2019.
[2] McKinsey & Company. (2016). Supply Chain 4.0 - the next generation digital supply chain, ed, 2016.
[3] Research. D. T. (2019). Logistics Trend Radar, ed, 2019.
[4] Costello, K. (2019). Gartner Predicts the Future of AI Technologies, ed, 2019.
[5] Research, D. T. (2018). Artificial Intelligence in Logistics, ed, 2018.
[6] Lalla-Ruiz, E., Melián-Batista, B., Marcos Moreno-Vega, J. (2012). Artificial intelligence hybrid heuristic based on tabu search for the dynamic berth allocation problem, Engineering Applications of Artificial Intelligence, 25 (6), p 1132-1141, 2012, doi: https://doi.org/10.1016/j.engappai.2012.06.001.
[7] Zakeri, A., Saberi, M., Hussain, O. K., Chang, E. (2018). An early detection system for proactive management of raw milk quality: an australian case study, IEEE Access, 6, p 64333-64349, 2018, doi: 10.1109/ACCESS.2018.2877970.
[8] Guin, U., Huang, K., Dimase, D., Carulli, J. M., Tehranipoor, M., Makris, Y. (2014). Counterfeit integrated circuits: A rising threat in the global semiconductor supply chain, In: Proceedings of the IEEE, 102 (8), p1207-1228, 2014, doi:10.1109/ JPROC.2014.2332291.
[9] Dias, J. C. Q., Calado, J. M. F., Osório, A. L., Morgado, L. F. (2009). RFID together with multi-agent systems to control global value chains, Annual Reviews in Control, vol. 33, no. 2, p 185-195, 2009, doi: https://doi.org/10.1016/ j.arcontrol.2009.03.005.
[10] Wi’cek, P. (2016). Intelligent Approach to Inventory Control in Logistics under Uncertainty Conditions, Transportation Research Procedia, vol. 18, p 164-171, 2016, doi: https://doi.org/10.1016/j.trpro.2016.12.023.
[11] Thiel, M., Hinckeldeyn, J., Kreutzfeldt, J. (2018). Deep learning for 3D object recognition in logistics, Logistics Journal, vol. 2018, no. 1, 2018, doi: 10.2195/lj_Proc_thiel_de_201811_01.
[12] Arunachalam, D., Kumar, N., Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice, Transportation Research Part E: Logistics and Transportation Review, vol. 114, p 416-436, 2018, doi: https://doi.org/10.1016/j.tre.2017.04.001.
[13] Singh, A., Shukla, N., Mishra, N. (2018). Social media data analytics to improve supply chain management in food industries, Transportation Research Part E: Logistics and Transportation Review, vol. 114, pp. 398-415, 2018, doi: https://doi.org/10.1016/j.tre.2017.05.008.
[14] Govindan, K., Cheng, T. C. E., Mishra, N., Shukla, N. (2018). Big data analytics and application for logistics and supply chain management,” Transportation Research Part E: Logistics and Transportation Review, vol. 114, p 343-349, 2018, doi: https://doi.org/10.1016/j.tre.2018.03.011.
[15] Zeidler, F., Bayhan, H., Venkatapathy, A. K. R., Hompel, M. T. (2017). Reference field for research and development of novel hybrid forms of human machine interaction in logistics, Logistics Journal, 2017, doi: 10.2195/lj_Proc_zeidler_de_201710_01.
[16] Dereli, T., Das, G. S. (2010). Development of a decision support system for solving container loading problems, Transport, 25 (2), p 138-147+Ib+IIb, 2010, doi: 10.3846/transport.2010.17.
[17] Madigan, D., Mittal, S., Roberts, F. (2011). Efficient sequential decision-making algorithms for container inspection operations, Naval Research Logistics, 58 (7), p 637-654, 2011, doi: 10.1002/nav.20472.
[18] Abukhousa, E., Al-Jaroodi, J., Lazarova-Molnar, S., Mohamed, N. (2014). Simulation and modeling efforts to support decision making in healthcare supply chain management, The Scientific World Journal, 2014, doi: 10.1155/2014/354246.
[19] Zhao, M., Liu, X. (2018). Development of decision support tool for optimizing urban emergency rescue facility locations to improve humanitarian logistics management, Safety Science, 102, p110-117, doi: https://doi.org/10.1016/j.ssci.2017.10.007.
[20] Pratikakis, N. E., Realff, M. J., Lee, J. H. (2010). Strategic capacity decision-making in a stochastic manufacturing environment using real time approximate dynamic programming, Naval Research Logistics, 57 (3), p 211-224, 2010, doi: 10.1002/nav.20384.
[21] Wu, C.-L., Law, K. (2019). Modelling the delay propagation effects of multiple resource connections in an airline network using a Bayesian network model, Transportation Research Part E: Logistics and Transportation Review, 122, p 62-77, 2019, doi: https://doi.org/10.1016/j.tre.2018.11.004.
[22] Diana, T. (2018). Can machines learn how to forecast taxi-out time? A comparison of predictive models applied to the case of Seattle/Tacoma International Airport, Transportation Research Part E: Logistics and Transportation Review, vol. 119, p 149- 164, 2018, doi: https://doi.org/10.1016/j.tre.2018.10.003.
[23] Xie, G., Zhang, N., Wang, S. (2017). Data characteristic analysis and model selection for container throughput forecasting within a decomposition-ensemble methodology, Transportation Research Part E: Logistics and Transportation Review, vol. 108, pp. 160-178, 2017, doi: https://doi.org/10.1016/j.tre.2017.08.015.
[24] Nikolopoulos, K. I., Bavbai, M. Z., Bozos, K. (2016). Forecasting supply chain sporadic demand with nearest neighbor approaches, International Journal of Production Economics, vol. 177, p 139-148, 2016, doi: https://doi.org/10.1016/ j.ijpe.2016.04.013.
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[38] Pendharkar, P., Nanda, S. (2006). A misclassification costminimizing evolutionary-neural classification approach, Naval Research Logistics, 53 (5), p 432-447, 2006, doi: 10.1002/nav.20154.
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