论文标题
外包的每日包装问题的确切方法
An Exact Method for the Daily Package Shipment Problem with Outsourcing
论文作者
论文摘要
包装运输问题需要在运输中心网络(TCN)中为包装的最佳共同设计路径。由中国包装运输行业引起的实例通常涉及超过一万个原产地发生(OD)对,并且必须在一个小时内每天解决。由于由于其竞争关系而没有在不同的起源中心之间没有相互作用的互动,因此我们提出了一种新颖的两层局部包装运输(LPS-TCN)模型,该模型利用外包来节省成本。因此,原始问题分解为一组较小的装运问题,每个问题都有数百个OD对,随后被建模为混合整数程序(MIP)。由于LPS-TCN模型被证明是强烈的NP螺旋,并且包含数以万计的可行路径,因此现成的MIP求解器无法在实际上可接受的时间内产生可靠的解决方案。我们开发了一种基于列的基于列的算法,该算法迭代地添加了“有利可图的”路径,并通过特定于问题的切割平面和可变的绑定拧紧技术来进一步增强它。关于中国主要包装公司的现实实例的计算实验表明,LPS-TCN模型可以产生解决方案,使整个TCN的每日经济成本降低到100万CNY。此外,我们提出的算法求解了LPS-TCN模型的速度要比CPLEX(最先进的商业MIP求解器之一)快得多。
The package shipment problem requires to optimally co-design paths for both packages and a heterogeneous fleet in a transit center network (TCN). Instances arising from the package delivery industry in China usually involve more than ten thousand origin-destination (OD) pairs and have to be solved daily within an hour. Motivated by the fact that there is no interaction among different origin centers due to their competitive relationship, we propose a novel two-layer localized package shipment on a TCN (LPS-TCN) model that exploits outsourcing for cost saving. Consequently, the original problem breaks into a set of much smaller shipment problems, each of which has hundreds of OD pairs and is subsequently modelled as a mixed integer program (MIP). Since the LPS-TCN model is proved to be Strongly NP-hard and contains tens of thousands of feasible paths, an off-the-shelf MIP solver cannot produce a reliable solution in a practically acceptable amount of time. We develop a column generation based algorithm that iteratively adds "profitable" paths and further enhance it by problem-specific cutting planes and variable bound tightening techniques. Computational experiments on realistic instances from a major Chinese package express company demonstrate that the LPS-TCN model can yield solutions that bring daily economic cost reduction up to 1 million CNY for the whole TCN. In addition, our proposed algorithm solves the LPS-TCN model substantially faster than CPLEX, one of the state-of-the-art commercial MIP solvers.