# Learning Optimal Crew Dispatch for Grid Restoration Following an Earthquake

arXiv: https://arxiv.org/abs/2509.04308 · HTML: https://mlsanity.com/paper/2509.04308/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2025.

## Built on by (most cited first, 6 of 6)

- [Event-Driven Deep RL Dispatcher for Post-Storm Distribution System Restoration](https://mlsanity.com/paper/2601.10044/) (2026)
- [An Integrated Two-Stage Deep-Learning Tool for Rapid Post-Hurricane Damage Identification and Repair Scheduling](https://mlsanity.com/paper/2606.29117/) (2026)
- [Heterogeneous Multi-Agent Proximal Policy Optimization for Power Distribution System Restoration](https://mlsanity.com/paper/2511.14730/) (2025)
- [A Deep Reinforcement Learning (DRL)-Based Transformer Method for Solving the Open Shop Scheduling Problem](https://mlsanity.com/paper/2606.13682/) (2026)
- [A Graph Neural Network--Guided Genetic Algorithm for Physical Internet Supply Chain Optimization under Cost Uncertainty](https://mlsanity.com/paper/2608.10245/) (2026)
- [Dynamic Multi-Depot Vehicle Routing with Online Requests: Event-Driven Transformer--DRL and Rolling-Horizon Benchmarking](https://mlsanity.com/paper/2608.13799/) (2026)

## Abstract (arXiv)

Post-disaster crew dispatch is a critical but computationally intensive task. Traditional mixed-integer linear programming methods often require minutes to several hours to compute solutions, leading to delays that hinder timely decision-making in highly dynamic restoration environments. To address this challenge, we propose a novel learning-based framework that integrates transformer architectures with deep reinforcement learning (DRL) to deliver near real-time decision support without compromising solution quality. Crew dispatch is formulated as a sequential decision-making problem under uncertainty, where transformers capture high-dimensional system states and temporal dependencies, while DRL enables adaptive and scalable decision-making. Earthquake-induced distribution network damage is first characterized using established seismic standards, followed by a scenario generation and reduction pipeline that aggregates probable outcomes into a single geospatial impact map. Conditioned on this map, the proposed framework generates second-level dispatch strategies, trained offline on simulated and historical events and deployed online for rapid response. In addition to substantial runtime improvements, the proposed method enhances system resilience by enabling faster and more effective recovery and restoration. Case studies, particularly on the 2869-bus European gas and power network, demonstrate that the method substantially accelerates restoration while maintaining high-quality solutions, underscoring its potential for practical deployment in large-scale disaster response.