Repair Crew Routing for Infrastructure Network Restoration under Incomplete Information

TL;DR

This paper introduces the Traveling Repairman Network Restoration Problem (TRNRP), modeling infrastructure repair under incomplete information and proposing reinforcement learning-based solutions with state aggregation to optimize repair routing.

Contribution

It formulates TRNRP as a Markov decision process and develops reinforcement learning methods with structural insights and state aggregation for efficient solutions.

Findings

Reinforcement learning effectively solves TRNRP.

State aggregation reduces computational complexity.

Proposed methods outperform benchmarks in simulations.

Abstract

This paper considers a disrupted infrastructure network where the repair crew knows the locations of service outages but not the locations of actual faults. Our goal is to determine a route for a single crew to visit and repair the disruptions to restore service with minimum negative impact. We call this problem the Traveling Repairman Network Restoration Problem (TRNRP). This problem presents strong computational challenges due to the combinatorial nature of the decisions, inter-dependencies within the underlying infrastructure network, and incomplete information. Considering the dynamic nature of the decisions as a result of dynamic information revelation on the status of the nodes, we model this problem as a finite-horizon Markov decision process. Our solution approach uses value approximation based on reinforcement learning, which is strengthened by structural results that identify a set of suboptimal moves. In addition, we propose state aggregation methods to reduce the size of the state space. We perform extensive computational studies to characterize the performance of our solution methods under different parameter settings and to compare them with benchmark solution approaches.