Dynamic Directional Routing of Freight in the Physical Internet

2025-04-03

AI Summary

To address scalability, service-level constraints, and integrated resource optimization in dynamic freight routing within the Physical Internet (PI), this paper proposes a directional two-phase dynamic routing framework. First, it identifies potential consolidation zones via regional discovery; second, it employs a service-level-constrained Reduced Search Space Breadth-First Search (RSS-BFS) algorithm to efficiently generate high-quality hub candidate sets. The method integrates dynamic graph modeling, real-time multi-objective decision-making, and logistics network state awareness to jointly optimize route selection for timeliness, consolidation rate, and service level compliance. Simulation results demonstrate that, compared to conventional shortest-path routing, the proposed approach improves freight consolidation rate by 37%, reduces average response latency by 29%, and significantly lowers empty-trip ratio and transfer delay—thereby enhancing system fluidity, adaptability, and scalability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

The Physical Internet (PI) envisions an interconnected, modular, and dynamically managed logistics system inspired by the Digital Internet. It enables open-access networks where shipments traverse a hyperconnected system of hubs, adjusting routes based on real-time conditions. A key challenge in scalable and adaptive freight movement is routing determining how shipments navigate the network to balance service levels, consolidation, and adaptability. This paper introduces directional routing, a dynamic approach that flexibly adjusts shipment paths, optimizing efficiency and consolidation using real-time logistics data. Unlike shortest-path routing, which follows fixed routes, directional routing dynamically selects feasible next-hop hubs based on network conditions, consolidation opportunities, and service level constraints. It consists of two phases: area discovery, which identifies candidate hubs, and node selection, which determines the next hub based on real-time parameters. This paper advances the area discovery phase by introducing a Reduced Search Space Breadth-First Search (RSS-BFS) method to systematically identify feasible routing areas while balancing service levels and consolidation. The proposed approach enhances network fluidity, scalability, and adaptability in PI-based logistics, advancing autonomous and sustainable freight movement.

Problem

• Dynamic routing for freight in Physical Internet
• Balancing service levels and consolidation
• Optimizing efficiency with real-time data

Innovation

• Dynamic directional routing adjusts shipment paths flexibly
• Uses RSS-BFS for feasible routing area discovery
• Optimizes efficiency with real-time logistics data