Enhancing Lateness Management in Cross-Docking

今天的市场比以往任何时候都发展得更快，公司面临着更频繁、更高效、更具成本效益地分销产品的挑战。这导致了全球供应链中交叉对接的兴起，以帮助跟上客户需求的步伐。交叉对接是指将货物或材料从入境车辆(如卡车、火车或船舶集装箱)上卸下，然后直接装载到出境车辆上，中间没有存储。一种常见的交叉对接操作形式对应于单个或多个项目的托盘，这些托盘被卸下，根据其目的地进行分类，然后直接放置到出境车辆上。这一策略使运输公司能够以更短的产品周期和更容易的产品定制，向更主动、更敏捷和更灵活的供应链迈进。虽然进出港车辆分配到对接码头的车辆已被广泛研究，但对延迟问题的研究很少，即(1)当进站车辆是集装箱时，集装箱迟交到船上，以及(2)迟交给客户。我们建议在将入境集装箱分配到门的整个过程中调查这些延误问题，以最大限度地减少因集装箱退货延迟而造成的延误惩罚，并通过与延迟或提前向客户交付货物相关的延误惩罚来缓解延误问题。我们将假设出境车辆的时间表将在与Engage助学金支持的硕士学生相关的研究项目中给出，而博士后研究员(即将提交的MITACSElevate)将采用灵活的卡车时间表。我们计划设计元启发式算法，它将集成一些机器学习工具，以便从过去的教训中受益，以便更好地估计旅行时间。设计和验证

Today's marketplace is moving faster than ever, and companies are challenged to distribute their products morequickly, efficiently and cost-effectively. This has led to the rise of cross docking in the global supply chain tohelp keep pace with customer demand. Cross-docking refers to the practice of unloading goods or materialsfrom an incoming vehicle (e.g., truck, train or vessel container) and then loading them directly onto outboundvehicles with no storage in between.A common form of cross-docking operations corresponds to single or multi-item pallets, which are unloaded,sorted based on their destination, and placed directly onto outbound vehicles. This strategy allowstransportation companies to move towards more proactive, agile and flexible supply chains, with shorterproduct cycles and easier product customization.While the assignment of inbound/outbound vehicles to cross-dock doors has been widely studied, very fewstudies exist on the lateness issues, i.e., (1) late return of containers to vessels when inbound vehicles arecontainers, and (2) late delivery to customers. We propose to investigate these lateness issues throughout theassignment of inbound containers to doors in order to minimize the tardiness penalties due to late containerreturns, and the mitigation of it with the tardiness penalties associated to late or early delivery of goods tocustomers. We will assume that the schedule of the outbound vehicles to be given in the research projectsassociated with the master students supported by the Engage grant, while the postdoctoral fellow (MITACSElevate to be submitted shortly) will assume flexible truck scheduling.We plan to design meta-heuristic algorithms, which will integrate some machine learning tools in order tobenefit from the lessons learned from the past in order to better estimate the travel times. Design and validation