Tabu search design for difficult forest management optimization problems
Bibliographic record
Abstract
A series of tabu search (TS) methods for solving the stand harvesting and road access optimization problem was developed and evaluated. This challenging forest management problem includes spatial constraints for maximum opening size, adjacency delay (green up), as well as timber-flow targets derived exogenously from a strategic planning process. The base harvest decision unit is the stand, and harvest blocks are created dynamically as adjacent stands are scheduled for treatments. The road network subproblem is solved using a fast heuristic method to select a minimum discounted cost set of road construction projects so that scheduled stands are accessible. The TS methods range from simple ones with fixed tabu tenure to an adaptive search with feedback mechanisms to control tabu tenure and to direct the search near constraint boundaries. It was found that while simple TS algorithms can find feasible solutions to the problem, these may be far from optimal. A good short-term memory strategy, constraint boundaries smoothed using penalty functions, and customized diversification moves were important design elements in the most successful TS algorithm for this problem. This paper points out the necessity to design the TS method carefully, since there are many possible TS designs and the design choices matter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".