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A decision support system for improved resource planning and truck routing at logistic nodes

Author

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  • Alessandro Hill

    (Hamburg University of Technology)

  • Jürgen W. Böse

    (Hamburg University of Technology)

Abstract

In this paper, we present an innovative decision support system that simultaneously provides predictive analytics to logistic nodes as well as to collaborating truck companies. Logistic nodes, such as container terminals, container depots or container loading facilities, face heavy workloads through a large number of truck arrivals during peak times. At the same time, truck companies suffer from augmented waiting times. The proposed system provides forecasted truck arrival rates to the nodes and predicted truck gate waiting times at the nodes to the truck companies based on historical data, economic and environmental impact factors. Based on the expected workloads, the node personnel and machinery can be planned more efficiently. Truck companies can adjust their route planning in order to minimize waiting times. Consequently, both sides benefit from reduced truck waiting times while reducing traffic congestion and air pollution. We suggest a flexible cloud based service that incorporates an advanced forecasting engine based on artificial intelligence capable of providing individual predictions for users on all planning levels. In a case study we report forecasting results obtained for the truck waiting times at an empty container depot using artificial neural networks.

Suggested Citation

  • Alessandro Hill & Jürgen W. Böse, 2017. "A decision support system for improved resource planning and truck routing at logistic nodes," Information Technology and Management, Springer, vol. 18(3), pages 241-251, September.
    • Handle: RePEc:spr:infotm:v:18:y:2017:i:3:d:10.1007_s10799-016-0267-3
    DOI: 10.1007/s10799-016-0267-3
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    References listed on IDEAS

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    1. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 10, pages 515-554, Elsevier.
    2. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    3. Itf, 2015. "The Impact of Mega-Ships," International Transport Forum Policy Papers 10, OECD Publishing.
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    Cited by:

    1. Loske, Dominic & Klumpp, Matthias, 2021. "Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics," International Journal of Production Economics, Elsevier, vol. 241(C).
    2. Valentin Carlan & Dries Naudts & Pieter Audenaert & Bart Lannoo & Thierry Vanelslander, 2019. "Toward implementing a fully automated truck guidance system at a seaport: identifying the roles, costs and benefits of logistics stakeholders," Journal of Shipping and Trade, Springer, vol. 4(1), pages 1-24, December.
    3. Dornemann, Jorin & Rückert, Nicolas & Fischer, Kathrin & Taraz, Anusch, 2020. "Artificial intelligence and operations research in maritime logistics," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conferen, volume 30, pages 337-381, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

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