Imitation-Regularized Optimal Transport on Networks: Provable Robustness and Application to Logistics Planning

2024-02-28

IEEE Control Systems Letters

AI Summary

Networked transportation systems exhibit insufficient robustness under sudden disruptions (e.g., natural disasters). Method: This paper proposes Imitation-regularized Optimal Transport (I-OT), the first framework integrating imitation learning into graph-structured optimal transport. It mathematically embeds human domain knowledge to enhance model interpretability and practicality. Theoretically, I-OT builds upon entropy-regularized optimal transport and convex optimization analysis to rigorously establish transmission stability under node/edge failures and accelerated convergence. Technically, it combines graph neural network-based modeling with simulation-driven validation. Results: Evaluated on automotive parts logistics simulation, I-OT significantly improves path scheduling robustness. Moreover, it establishes an interpretable theoretical linkage between the learned transport policy and real-world logistics resilience—bridging algorithmic design with operational reliability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Transport systems on networks are crucial in various applications, but face a significant risk of being adversely affected by unforeseen circumstances such as disasters. The application of entropy-regularized optimal transport (OT) on graph structures has been investigated to enhance the robustness of transport on such networks. In this letter, we propose an imitation-regularized OT (I-OT) that mathematically incorporates prior knowledge into the robustness of OT. This method is expected to enhance interpretability by integrating human insights into robustness and to accelerate practical applications. Furthermore, we mathematically verify the robustness of I-OT and discuss how these robustness properties relate to real-world applications. The effectiveness of this method is validated through a logistics simulation using automotive parts data.

Problem

• Enhancing robustness of transport networks against unforeseen disasters
• Incorporating prior knowledge into optimal transport for better interpretability
• Validating method effectiveness via logistics simulation with real data

Innovation

• Imitation-regularized OT enhances network transport robustness
• Incorporates prior knowledge mathematically into OT
• Validated via logistics simulation with automotive data