RuttOpt — a decision support system for routing of logging trucks
Bibliographic record
Abstract
We describe the decision support system RuttOpt, which is developed for scheduling logging trucks in the forest industry. The system is made up of a number of modules. One module is the Swedish road database NVDB, which consists of detailed information of all of the roads in Sweden. This also includes a tool to compute distances between locations. A second module is an optimization routine that finds a schedule, i.e., set of routes for all trucks. This is based on a two-phase algorithm where linear programming and a standard tabu search method are used. A third module is a database storing all relevant information. At the center of the system is a user interface where information and results can be viewed on maps, Gantt schedules, and result reports. The RuttOpt system has been used in a number of case studies and we describe four of these. The case studies have been made in both forest companies and hauling companies. The cases range from 10 to 110 trucks and with a planning horizon ranging between 1 and 5 days. The results show that the system can be used to solve large case studies and that the potential savings are in the range 5%–30%.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".