| Authors | Bettinger et al. |
| Paper (PDF) | View paper |
| Category | Planning & Inventory › Remote sensing & mapping |
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AI-driven harvest scheduling refers to the application of artificial intelligence and machine learning techniques to the complex optimisation problem of determining when, where, and in what sequence timber stands should be harvested to achieve a balanced set of silvicultural, economic, logistical, and environmental objectives. Traditional harvest scheduling relied on linear programming models and heuristic approaches that, while mathematically rigorous, often struggled to incorporate the full complexity of real-world constraints such as road access windows, machine availability, wood quality variation, market price volatility, and ecological protection requirements. Modern AI approaches — including reinforcement learning, genetic algorithms, constraint satisfaction solvers, and deep neural networks — can process far larger problem spaces and integrate dynamic, real-time data inputs that static models cannot handle. For example, a reinforcement learning agent can be trained to develop multi-year harvest sequences that maximise net present value while respecting biodiversity buffers, streamside exclusion zones, and haul distance limits, adapting its recommendations as new inventory data, machine breakdowns, or changed market conditions arise. In Australia, harvest scheduling is particularly complex due to the need to balance commercial timber production with conservation obligations under regional forest agreements, fire risk management priorities, and variable site conditions across diverse forest types. Companies and government forest managers are increasingly exploring AI scheduling tools to improve wood flow predictability, reduce periods of feast-or-famine for mill supply chains, and demonstrate to regulators and the public that harvesting is being conducted in a planned and sustainable manner. Integration with real-time machine telemetry, weather forecasting, and mill inventory systems is emerging as the next frontier in AI harvest scheduling capability.
| Field | Value |
|---|---|
| Company | Bettinger et al. |
| Type | Paper |
| Year | 2024 |
| FWPA RD&E | 4.1 |