Risk-aware scheduling for post-wildfire salvage logging under inventory estimation uncertainty

Wildfires intensified by climate change pose growing risks to forestry, forcing landowners to make high-stakes decisions about salvaging damaged timber under inventory uncertainty. This paper presents a stochastic optimization model for salvage logging that integrates chance constraints into a time-indexed mixed-integer programming framework. To manage inventory estimation errors, we utilize a grid-based linearized formulation of Chernoff bounds, enabling tractable control over the probability of exceeding harvesting capacity limits. The model allocates workforce and processing resources while balancing harvesting profits and insurance payouts via a tunable risk parameter. We evaluate the model using Chilean forestry data and simulated instances, the model outperforms deterministic baselines in financial robustness. This work demonstrates how risk-aware optimization supports resilient resource planning in post-disaster environments and broader contexts involving time-critical trade-offs.