Heavy Good Vehicles (HGVs) are the second largest source of greenhouse gas emissions in transportation, after cars and taxis. However, HGVs are inefficiently utilised, with more than one-third of their weight capacity not being used during travel. We, thus, in this paper address collaborative logistics, an effective pathway to enhance HGVs' utilisation and reduce carbon emissions. We investigate a multi-agent system approach to facilitate collaborative logistics, particularly carrier collaboration. We propose a simple yet effective multi-agent collaborative logistics (MACL) framework, representing key stakeholders as intelligent agents. Furthermore, we utilise the MACL framework in conjunction with a proposed system architecture to create an integrated collaborative logistics testbed. This testbed, consisting of a physical system and its digital replica, is a tailored cyber-physical system or digital twin for collaborative logistics. Through a demonstration, we show the utility of the testbed for studying collaborative logistics.

翻译：重型货车（HGVs）是交通运输领域仅次于小汽车和出租车的第二大温室气体排放源。然而，重型货车的利用率低下，行驶过程中超过三分之一的载重能力未被使用。因此，本文针对协同物流这一提升重型货车利用率并减少碳排放的有效途径展开研究。我们探索了一种基于多智能体系统的方法来促进协同物流，特别是承运商之间的协作。我们提出了一个简洁而高效的多智能体协同物流（MACL）框架，将关键利益相关者表示为智能体。此外，我们将该MACL框架与所提出的系统架构相结合，构建了一个集成的协同物流试验平台。该试验平台由物理系统及其数字副本组成，是一个针对协同物流定制的信息物理系统或数字孪生体。通过示范案例，我们展示了该试验平台在研究协同物流方面的实用性。