Frontiers in Operations: Prioritizing Disaster Recovery Under Budget Uncertainty

Problem definition: In the aftermath of disasters, governments must make urgent decisions about how to deploy limited resources for recovery, such as restoring roads or siting emergency facilities. However, these actions often need to be taken before the amount and timing of external funding (e.g., federal disaster relief) are known. This mismatch between the need for immediacy and the delay in budget realization poses a fundamental challenge: How can agencies prioritize recovery actions when budgets are uncertain and decisions, once made, are irreversible? Methodology/results: We develop a practical planning framework that produces a fixed priority list of recovery actions, allowing agencies to act immediately and continue execution as funding arrives over time. The framework identifies early actions that perform well across a range of possible funding paths and preserve the value of later investments. The model is cast as a multiscenario mixed-integer linear program with monotonicity constraints, enforcing consistency in prioritization across all scenarios. To compute such a list efficiently, whereas the natural linear program relaxation of this formulation is weak, we introduce a pegging-based heuristic: For each scenario, we solve the optimal 0-1 allocation, fix it, and relax the remaining scenarios into a linear program. Aggregating across all scenarios yields a robust and interpretable prioritization list. Our analysis provides performance guarantees for committing to a single priority list instead of waiting for full budget information. We derive explicit bounds on the expected performance loss of any prioritization strategy relative to a full-information hindsight benchmark. These results show that, under modest assumptions, the loss from committing to a single priority list is provably small. Furthermore, our pegging-based heuristic yields approximation guarantees under mild conditions and performs remarkably well in empirical evaluations. Managerial implications: This framework offers disaster response planners a rigorous and practical tool for making irreversible decisions under budget uncertainty. The main insight is that the best early action is not always the one that gives the largest immediate gain, but the one that positions the system best when additional funding becomes available. Through experiments on synthetic data and a real-world road network in Manhattan, we demonstrate that the proposed prioritization strategy consistently outperforms conventional heuristics and closely approximates the performance of an ideal benchmark with full budget information. The results highlight the potential of our approach to support timely, resilient, and high-quality disaster recovery planning under uncertain funding conditions. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72422006] and the Hong Kong Research Grants Council Theme-based Research Scheme [Grant T32-615/24-R]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2024.1574 .