论文标题
在雷诺解决大陆规模的库存路由问题
Solving a Continent-Scale Inventory Routing Problem at Renault
论文作者
论文摘要
本文是与雷诺合作的果实。他们的后勤需要解决大陆规模的多属性库存路由问题（IRP）。我们的实例平均有30个商品，16个仓库和600个客户分布在整个大陆上，比文献中的数量级大。现有算法不会扩展。我们提出了一个大型邻里搜索（LNS）。为了使它起作用，（1）我们将现有的分流式车辆路线问题和IRP社区推广到这种情况下，（2）我们将中等规模IRP的最先进的数学家变成了一个大社区，（3）我们介绍了两个新颖的扰动：客户的重新插入，而商品的重新插入为IRP解决方案。我们还根据流量松弛得出了新的下限。为了刺激大型IRP的研究，我们介绍了工业实例库。我们在这些实例上基准了我们的算法，并将代码开源。广泛的数值实验突出了我们LN的每个组件的相关性。
This paper is the fruit of a partnership with Renault. Their backward logistic requires solving a continent-scale multi-attribute inventory routing problem (IRP). With an average of 30 commodities, 16 depots, and 600 customers spread across a continent, our instances are orders of magnitude larger than those in the literature. Existing algorithms do not scale. We propose a large neighborhood search (LNS). To make it work, (1) we generalize existing split delivery vehicle routing problem and IRP neighborhoods to this context, (2) we turn a state-of-the art matheuristic for medium-scale IRP into a large neighborhood, and (3) we introduce two novel perturbations: the reinsertion of a customer and that of a commodity into the IRP solution. We also derive a new lower bound based on a flow relaxation. In order to stimulate the research on large-scale IRP, we introduce a library of industrial instances. We benchmark our algorithms on these instances and make our code open-source. Extensive numerical experiments highlight the relevance of each component of our LNS.
