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

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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.

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Notes

  1. Intelligent logistic order arrival decision support.

  2. The MathWorks, Inc. (http://www.mathworks.com/help/nnet).

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Acknowledgments

The research Project 17694 N, entitled “Truck Waiting Time Forecasting at Logistic Nodes” (Lkw-Wartezeitprognose für logistische Knoten) at the Institute of Maritime Logistics at Hamburg University of Technology was funded by the German Federal Ministry for Economic Affairs and Energy (Vorhaben der Industriellen Gemeinschaftsförderung, IGF). We thank Sabine Werner for the fruitful discussion.

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  1. Institute of Maritime Logistics, Hamburg University of Technology, Am Schwarzenberg-Campus 4 (D), 21073, Hamburg, Germany

    Alessandro Hill & Jürgen W. Böse

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  1. Alessandro Hill
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  2. Jürgen W. Böse
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Correspondence to Alessandro Hill.

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Hill, A., Böse, J.W. A decision support system for improved resource planning and truck routing at logistic nodes. Inf Technol Manag 18, 241–251 (2017). https://doi.org/10.1007/s10799-016-0267-3

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