Learning-aided Bigraph Matching Approach to Multi-Crew Restoration of Damaged Power Networks Coupled with Road Transportation Networks

2025-06-24

AI Summary

This paper addresses the multi-crew coordinated repair scheduling problem for coupled post-disaster power–transportation networks. We propose a restoration planning method integrating bipartite graph matching and graph reinforcement learning. Specifically, we construct a heterogeneous graph model to explicitly capture cross-network interdependencies; design a learning-driven reward function; and perform end-to-end training by jointly leveraging graph neural networks, proximal policy optimization, and neuroevolution. Task allocation is further accelerated via a precomputed path-simulation environment and weighted bipartite graph maximum matching. The method exhibits strong generalizability across diverse scenarios and high scalability. Evaluated on a joint IEEE 8500-node power grid and 21 km² transportation network case study, it achieves threefold higher repair efficiency than random policies and significantly outperforms conventional optimization approaches, while reducing computational time by several orders of magnitude.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Social networks and social learning

Abstract

The resilience of critical infrastructure networks (CINs) after disruptions, such as those caused by natural hazards, depends on both the speed of restoration and the extent to which operational functionality can be regained. Allocating resources for restoration is a combinatorial optimal planning problem that involves determining which crews will repair specific network nodes and in what order. This paper presents a novel graph-based formulation that merges two interconnected graphs, representing crew and transportation nodes and power grid nodes, into a single heterogeneous graph. To enable efficient planning, graph reinforcement learning (GRL) is integrated with bigraph matching. GRL is utilized to design the incentive function for assigning crews to repair tasks based on the graph-abstracted state of the environment, ensuring generalization across damage scenarios. Two learning techniques are employed: a graph neural network trained using Proximal Policy Optimization and another trained via Neuroevolution. The learned incentive functions inform a bipartite graph that links crews to repair tasks, enabling weighted maximum matching for crew-to-task allocations. An efficient simulation environment that pre-computes optimal node-to-node path plans is used to train the proposed restoration planning methods. An IEEE 8500-bus power distribution test network coupled with a 21 square km transportation network is used as the case study, with scenarios varying in terms of numbers of damaged nodes, depots, and crews. Results demonstrate the approach's generalizability and scalability across scenarios, with learned policies providing 3-fold better performance than random policies, while also outperforming optimization-based solutions in both computation time (by several orders of magnitude) and power restored.

Problem

• Optimizing crew allocation for power grid restoration
• Integrating transportation and power networks for efficient planning
• Enhancing resilience with learning-aided bigraph matching

Innovation

• Graph reinforcement learning for crew-task matching
• Heterogeneous graph merging power and transportation networks
• Neuroevolution and PPO for training graph neural networks