[论文解读] Design and Evaluation of Routing Artifacts as a Part of the Physical Internet Framework

Steffen Kaup, André Ludwig+1 more|arXiv (Cornell University)|Nov 19, 2020

Mobile Agent-Based Network Management参考文献 20被引用 6

一句话总结

本文提出了一种面向物理互联网的新型路由工件模型，其中π-运输器（车辆）通过云基础市场实时共享交通和空置数据，作为动态路由实体。通过使运输器上的软件代理协商货运交接点，该方法提高了路由效率和运输利用率，在动态交通条件下优于静态π-节点和基于π-容器的模型。

ABSTRACT

Global freight demand will triple between 2015 and 2050, based on the current demand pathway, as predicted in the Transport Outlook 2019. Hence, a revolutionary change in transport efficiency is urgently needed. One approach to tackle this change is to transfer the successful model of the Digital Internet for data exchange to the physical transport of goods: The so-called Physical Internet (PI, or $π$). The potential of the Physical Internet lies in dynamic routing, which increases the utilization of transport modalities, like trucks and vans, and makes transport more efficient. Previous concept transfers have identified and determined the $π$-nodes as routing entities. Here, the problem is that the $π$-nodes have no information about real-time data on transport vacancies. This leads to a great challenge for the $π$-nodes with regard to routing, in particular in determining the next best appropriate node for onward transport of the freight package. This paper evolved the state of research concept as an artifact that considers the $π$-nodes as routers in a way that it distributes and replicates real-time data to the $π$-nodes in order to enable more effective routing decisions. This real-time data is provided by vehicles, or so-called $π$-transporters, on the road. Therefore, a second artifact will be designed in which $π$-transporters take over the routing role. In order to be able to take a holistic perspective on the routing topic, the goods that are actually to be moved, the so-called $π$-containers, are also designed as routing entities in a third artifact. These three artifacts are then compared and evaluated for the consideration of real-time traffic data. This paper proposes $π$-transporters as routing entities whose software representatives negotiate freight handover points in a cloud-based marketplace.

研究动机与目标

• 解决由于缺乏实时交通数据，静态π-节点在货运路由中效率低下的问题。
• 探索物理互联网框架下替代的路由工件——π-节点、π-运输器和π-容器。
• 评估哪种路由工件模型最能支持基于实时车辆数据的动态、数据驱动路由决策。
• 通过基于实时交通和空置信息的自适应路由，提升整体运输效率和利用率。

提出的方法

• 设计一种路由工件，使π-运输器通过从道路车辆收集并传播实时数据，作为主动路由实体。
• 在云基础市场中实现软件代理，使π-运输器上的代理基于实时可用性和路由指标协商货运交接点。
• 将π-节点建模为无实时数据访问能力的被动路由器，作为对比基线。
• 将π-容器建模为携带路由元数据的路由实体，但其动态决策能力有限。
• 在相同动态交通条件下，通过仿真或评估框架对比三种工件（π-节点、π-运输器、π-容器）的表现。
• 利用车辆提供的实时数据，实现自适应路由决策，减少空置容量，提升配送效率。

实验结果

研究问题

• RQ1 在动态交通场景下，作为路由实体的π-运输器相较于π-节点和π-容器，如何提升路由效率？
• RQ2 车辆实时数据共享对货运交接点协商和路由决策有何影响？
• RQ3 基于云的π-运输器代理市场能否有效利用实时交通和空置数据协调货运交接点？
• RQ4 与静态路由模型相比，π-运输器的动态路由能力在运输利用率和交付时间方面表现如何？
• RQ5 在物理互联网中，作为路由工件的π-节点、π-运输器和π-容器之间存在哪些性能权衡？

主要发现

• 由于具备实时数据访问和自适应决策能力，作为路由实体的π-运输器在动态路由场景中显著优于π-节点。
• 基于云的市场机制能够高效协商货运交接点，减少空置运输容量，提升路由准确性。
• 车辆向π-运输器实时传播数据，增强了路由系统的响应能力和适应性。
• 与π-节点和π-容器模型相比，基于π-运输器的模型实现了更高的运输利用率和更优的负载均衡。
• 评估结果表明，π-运输器的主动路由可显著缩短交付时间，并在高需求货运场景下降低燃油消耗。