Forest harvesting operational planning tools: a systematic review of optimization, simulation, and spatial decision support systems
Bibliographic record
Abstract
Sustainable forest management relies on effective operational planning to ensure that harvesting practices support long-term objectives. Operations research methods have largely been used to support operational decision-making in forest harvest planning but the broader strengths, limitations and barriers to adoption remain unclear. This review addresses this gap by synthesizing existing research on operational planning tools for forest harvesting. Using PRISMA protocols, we conducted systematic searches in Scopus and Web of Science and identified 23 peer-reviewed studies published between 2005 and 2024. The included studies employed diverse approaches across geographic regions, most commonly being mixed-integer programming and geographic information systems (GIS). Results show that while these models provide valuable insights and demonstrate technical expertise, they are often hard-coded to specific sites, lack reproducibility and are rarely open-source. Developing modular, transparent and user-centric tools could strengthen the existing connection between research and practice, enabling forest planners to manage uncertainty and improve efficiency while aligning with broader sustainability goals. Our findings highlight the importance of designing adaptable frameworks that embed site-specificity as a structural element rather than a limitation. We synthesize findings into a practitioner checklist, covering inputs, constraints, solution approach, validation, user experience and openness to guide tool design and evaluation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".