Lookahead strategies for planning of post-disaster emergency restoration of road networks

Post-disaster emergency restoration (ER) has emerged as a promising approach to enhancing the resilience of critical infrastructure systems (CISs). However, devising optimal ER plans immediately after real-world disasters is inherently challenging due to the vast state space of modern CISs. To address this challenge, this paper proposes a range of strategies to guide these campaigns, with a particular focus on road networks (RNs) under earthquake disasters. Initially, a set of heuristic-based, easy-to-interpret strategies has been developed. Building on prior research, this study investigates the integration of lookahead search, examining its potential to refine and adapt these heuristics to meet diverse optimization objectives of ER campaigns. To operationalize these strategies, a multi-agent-based model (MABM) is established, wherein each restoration group is modelled as an autonomous agent, guided by the proposed planning strategies. The applicability of the model is demonstrated by its implementation in a real-world RN under catastrophic earthquake scenarios. The impact of various strategies on the effectiveness of the ER campaign is examined and elucidated. Notably, the planning strategy that combines a newly developed, accessibility-based heuristic with lookahead proves effective in sequentially balancing the trade-off between the accessibility and criticality of collapsed bridges. Based on the case study result, this approach consistently fulfils diverse optimization objectives across a range of earthquake scenarios, establishing it as the benchmark planning strategy of the post-shock ER of RNs.