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Inbound and outbound flow integration for cross-docking operations
European Journal of Operational Research ( IF 6.0 ) Pub Date : 2021-02-15 , DOI: 10.1016/j.ejor.2021.02.031
Marc-Antoine Coindreau , Olivier Gallay , Nicolas Zufferey , Gilbert Laporte

We consider the optimization of the cross-docking operations at three INtermodal LOgistics Platforms (INLOPs) of a large European car manufacturer (ECM). The planning horizon is a week and the time bucket is a day. An inbound flow of products is gradually received over the week by truck from inland suppliers, and has to be loaded into containers which are then shipped to offshore production plants. The full content of a container must be available at the INLOP to enable its loading operations to start, hence temporary storage is needed. The objective is to minimize an inventory penalty, computed as the largest daily volume of temporary product storage observed over the planning horizon. The current practice at ECM is to first optimize the content of the inbound trucks and of the outbound containers independently, and then determine the loading day of each container to be shipped based on these fixed contents. We propose to integrate, within the same optimization framework, the decisions on both truck and container contents, which involve complex loading constraints related to the dimensions and weights of the products, with those on the scheduling of container loading. We model the resulting problem as a mixed integer linear program, and we develop a decomposition scheme for it, as well as a fix-and-optimize matheuristic. We perform extensive computational experiments on real instances provided by ECM. Results show that a combination of these two matheuristics is able to generate solutions that reduce the average inventory penalty by 40%.



中文翻译:

用于越库作业的入库和出库流集成

我们考虑对一家大型欧洲汽车制造商 (ECM) 的三个多式联运物流平台 (INLOP) 的越库操作进行优化。计划范围是一周,时间段是一天。一周内通过卡车从内陆供应商处逐渐接收到产品的入库流,必须将其装入集装箱,然后运往海外生产工厂。容器的全部内容必须在 INLOP 处可用才能启动其加载操作,因此需要临时存储。目标是最大限度地减少库存损失,计算为在计划范围内观察到的最大每日临时产品存储量。ECM目前的做法是首先独立优化进站卡车和出站集装箱的内容,然后根据这些固定的内容确定每个集装箱的装货日期。我们建议在同一个优化框架内,将卡车和集装箱内容的决策(涉及与产品尺寸和重量相关的复杂装载约束)与集装箱装载调度的决策相结合。我们将由此产生的问题建模为混合整数线性程序,并为其开发分解方案,以及修复和优化数学算法。我们对 ECM 提供的真实实例进行了广泛的计算实验。结果表明,这两种数学算法的组合能够生成将平均库存损失减少 40% 的解决方案。在相同的优化框架内,对卡车和集装箱内容的决策,包括与产品尺寸和重量相关的复杂装载约束,以及关于集装箱装载调度的决策。我们将由此产生的问题建模为混合整数线性程序,并为其开发分解方案,以及修复和优化数学算法。我们对 ECM 提供的真实实例进行了广泛的计算实验。结果表明,这两种数学算法的组合能够生成将平均库存损失减少 40% 的解决方案。在相同的优化框架内,对卡车和集装箱内容的决策,包括与产品尺寸和重量相关的复杂装载约束,以及关于集装箱装载调度的决策。我们将由此产生的问题建模为混合整数线性程序,并为其开发分解方案,以及修复和优化数学算法。我们对 ECM 提供的真实实例进行了广泛的计算实验。结果表明,这两种数学算法的组合能够生成将平均库存损失减少 40% 的解决方案。我们将由此产生的问题建模为混合整数线性程序,并为其开发分解方案,以及修复和优化数学算法。我们对 ECM 提供的真实实例进行了广泛的计算实验。结果表明,这两种数学算法的组合能够生成将平均库存损失减少 40% 的解决方案。我们将由此产生的问题建模为混合整数线性程序,并为其开发分解方案,以及修复和优化数学算法。我们对 ECM 提供的真实实例进行了广泛的计算实验。结果表明,这两种数学算法的组合能够生成将平均库存损失减少 40% 的解决方案。

更新日期:2021-02-15
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