Deep reinforcement learning-based resilience optimization for infrastructure networks restoration with multiple crews  

作　　者：Qiang FENG Qilong WU Xingshuo HAI Yi REN Changyun WEN Zili WANG 

机构地区：[1]School of Reliability and Systems Engineering,Beihang University,Beijing 100191,China [2]Hangzhou International Innovation Institute,Beihang University,Hangzhou 311115,China [3]School of Electrical and Electronic Engineering,Nanyang Technological University,Singapore 639798,Singapore

出　　处：《Frontiers of Engineering Management》2025年第1期141-153,共13页工程管理前沿(英文版)

摘　　要：Restoration of infrastructure networks(INs)following large disruptions has received much attention lately due to examples of massive localized attacks.Within this challenge are two complex but critical problems:repair route identification and optimizing the sequence of the repair actions for resilience improvement.Existing approaches have not,however,given due consideration to globally optimal enhancement in resilience,especially with multiple repair crews that have uneven capacities.To address this gap,this paper focuses on a resilience opti-mization(RO)strategy for coordinating multiple crews.The objective is to determine the optimal routes for each crew and the best sequence of repairs for damaged nodes and links.Given the two-layered decision-making required—coordinating between multiple crews and opti-mizing each crew's actions—this study develops a deep reinforcement learning(DRL)framework.The framework leverages an actor-critic neural network that processes IN damage data and guides Monte Carlo tree search(MCTS)to identify optimal repair routes and actions for each crew.A case study based on the 228-node power grid,simulated using Python,demonstrates that the proposed DRL approach effectively supports restoration decision-making.

关 键 词：Infrastructure network restoration Deep reinforcement learning Resilience optimization Repair routing Multiple crews 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]