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.

卡车运输占英国汽车碳排放量的19%，但其中30%的卡车是空的，其余大部分都没有满负荷运行。减少空仓或“空运”的运输将对碳排放产生直接和重大的影响，并降低成本。该提案优化了轴辐式物流的负载系数（所用容量的百分比），特别是针对合作伙伴组织Pallet-Track。轴辐式网络使运输商或“仓库”能够将货物从其所在地区的客户运送到可能位于该国另一端甚至国外的收件人。仓库A将托盘带到最近的仓库或“中心”，在那里它们被来自目标交付区域的仓库拾取。与此同时，仓库A将从其他地方的仓库提取运往本区域的货物。这种运输模式减少了仓库的行程距离，并扩大了其交付范围。然而，这带来了一个不同的问题：他们不知道到达枢纽时需要收取多少货物。如果交付和收集数量不匹配，他们将运输空的空间或他们将没有足够的卡车，这是由于在枢纽错误估计托盘数量而加剧的。该提案使用机器学习来预测仓库需要处理的托盘数量以及网络枢纽在管理成员之间的流量方面的作用。人工智能将在决策的认知模型中解释这些数字，帮助仓库相互协作以共享资源。仓库交付和收集的托盘数量将被分析，以准确预测每个仓库一年中的每一天。认知模型将解释这些预测，并将其转化为优化负载因素的运输决策。它将计算出不同尺寸的卡车的哪种组合最能运送和收集自己的托盘。与此同时，它还可以向邻近的仓库标记任何可供其使用的闲置产能，或请求使用邻近仓库可能拥有的闲置产能。这项创新技术将优化卡车装载，减少碳排放，降低成本，使托盘轨道成为更具吸引力的轴辐式网络。它还将节省枢纽的空间，这将增加托盘的容量和吞吐量，减少交货时间。