Carbon and cost reduction for hub-and-spoke logistics

轴辐式物流的碳排放和成本降低

• 批准号: 10081321
• 金额: $ 6.28万
• 依托单位: EGRIST LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10081321
• 关键词: Carbon; cost; reduction; hub; spoke

Lorry transportation accounts for 19% of vehicle carbon emissions in the UK but 30% of those lorries are empty, with most of the remainder not running at capacity. Reducing haulage of empty space or "air" would have a direct and substantial impact on carbon emissions as well as bringing down costs.This proposal optimises the load factor (percentage of capacity used) for hub-and-spoke logistics and, specifically, for the partner organisation, Pallet-Track. Hub-and-spoke networks enable hauliers or "depots" to deliver goods from customers in their own areas to recipients who may be located at the other end of the country or even abroad. Depot A takes pallets to the nearest warehouse or "hub", where they are picked up by depots coming from the target delivery area. At the same time, Depot A will pick up goods destined for its own area that have been dropped off by depots from elsewhere.This transport model reduces distances depots travel and extends their delivery range. However, it creates a different problem: they do not know how much freight they will need to collect when they reach the hub. If the delivery and collection numbers do not match, they will transport empty space or they will not have enough lorries, which is compounded by incorrectly estimated pallet numbers at the hub.The proposal uses machine learning to predict the number of pallets depots need to process and the network hub's role in managing the flows between members. Artificial intelligence will interpret these numbers within a cognitive model of decision making that helps depots collaborate with each other to share resources.The numbers of pallets delivered and collected by depots will be analysed to produce accurate predictions for each depot, every day of the year. The cognitive model will interpret these predictions and translate them into transport decisions that optimise the load factors. It will work out which combination of lorries of different sizes are best able to deliver and collect their own pallets. At the same time, it will flag up to neighbouring depots any spare capacity that could be used by them or requests to use spare capacity the neighbours may have.This innovative technology will optimise lorry loading, reduce carbon emissions, and cut costs, making Pallet Track a more attractive hub-and-spoke network. It will also save space at the hubs, which will increase capacity and the throughput of pallets, reducing delivery times.

货车运输占英国车辆碳排放量的19％，但其中30％的卡车是空的，其余的大部分未能运行。减少空白或“空气”的运输将对碳排放产生直接而实质性的影响，并降低成本。该提案优化了轮毂和辐条物流的负载因子（所使用的容量百分比），尤其是合作伙伴组织Pallet-Track。集线器和辐条网络使运输者或“仓库”从其自身地区的客户交付商品到可能位于该国另一端甚至国外的接收者。仓库A将托盘带到最近的仓库或“枢纽”，在那里，它们是由来自目标输送区域的仓库捡起的。同时，仓库A将接收到自己区域的货物，这些货物已被其他地方撤离。这种运输模型减少了距离仓库的旅行并扩展了其交付范围。但是，它会造成一个不同的问题：他们不知道到达集线器时需要收集多少货物。如果交付和收集号不匹配，他们将运输空白空间，否则将没有足够的卡车，这会因枢纽的错误估计的托盘数量而加重。该提案使用机器学习来预测托盘库的数量需要处理以及网络中心在成员之间管理流量中的作用。人工智能将在决策制定的认知模型中解释这些数字，该模型有助于仓库相互协作以共享资源。将分析由仓库交付和收集的托盘数量，以对每个仓库（每天的每一天）进行准确的预测。认知模型将解释这些预测，并将其转化为运输决策，以优化负载因子。它将解决哪种不同尺寸的卡车的组合最好能够交付和收集自己的托盘。同时，它将标记到附近的仓库，他们可以使用的任何备用容量或使用邻居可能拥有的备用容量的要求。这种创新的技术将优化货车装载，减少碳排放并削减成本，从而使托盘轨道追踪更具吸引力的Hub和opkek网络。它还将节省集线器的空间，这将增加容量和托盘的吞吐量，从而减少交货时间。