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A decision support system for improved resource planning and truck routing at logistic nodes
Information Technology and Management ( IF 2.310 ) Pub Date : 2016-10-03 , DOI: 10.1007/s10799-016-0267-3
Alessandro Hill , Jürgen W. Böse

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.

中文翻译:

决策支持系统,用于改进后勤节点的资源计划和卡车路线

在本文中,我们提出了一个创新的决策支持系统,该系统同时为物流节点以及合作的卡车公司提供预测分析。后勤节点(例如集装箱码头,集装箱仓库或集装箱装载设施)在高峰时段面临大量卡车到达的繁重工作量。同时,卡车公司的等待时间增加。所提出的系统基于历史数据,经济和环境影响因素,提供到节点的预测卡车到达率和到卡车公司的节点在卡车节点的等待时间。根据预期的工作量,可以更有效地计划节点人员和机器。卡车公司可以调整路线计划,以最大程度地减少等待时间。所以,双方都受益于减少的卡车等待时间,同时减少了交通拥堵和空气污染。我们建议使用基于云的灵活服务,其中应包含基于人工智能的高级预测引擎,该引擎能够为所有计划级别的用户提供个性化预测。在一个案例研究中,我们报告了使用人工神经网络在空集装箱仓库中卡车等待时间获得的预测结果。
更新日期:2016-10-03
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