Carbon and cost reduction for hub-and-spoke logistics

在英国，卡车运输占机动车碳排放的19%，但其中30%是空的，其余大部分都没有满负荷运行。减少空空间或“空气”的运输将对碳排放产生直接和实质性的影响，并降低成本。这项建议优化了轴辐式物流的负载率(已用容量的百分比)，特别是对于合作伙伴组织托盘-轨道。轴辐式网络使运输商或“仓库”能够将货物从所在地区的客户送到可能位于国家另一端甚至国外的收货人手中。仓库A将托盘运送到最近的仓库或“枢纽”，在那里它们由来自目标交货区的仓库提货。同时，A车场将从外地接收由车场投放到本地区的货物，这种运输模式缩短了车场的行程，扩大了车场的送货范围。然而，这带来了一个不同的问题：他们不知道当他们到达枢纽时需要收取多少运费。如果送货和收货数量不匹配，它们将运输空位或没有足够的卡车，再加上枢纽处错误估计的托盘数量。该提议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流动方面的角色。人工智能将在决策的认知模型中解释这些数字，帮助仓库相互协作共享资源。仓库将分析仓库交付和收集的托盘数量，以产生对每个仓库的准确预测，一年中的每一天。认知模型将解释这些预测，并将它们转化为优化载客率的运输决策。它将计算出不同大小的卡车的哪种组合最能运送和收集自己的托盘。与此同时，它将向邻近的仓库标明他们可以使用的任何闲置产能，或要求使用邻居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，并降低成本，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，这将增加容量和托盘吞吐量，缩短交货时间。

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.