A Decomposition-Based Framework for Large-Scale Multi-Period Log-Truck Routing and Scheduling: A Case Study in Canadian Forestry
Bibliographic record
Abstract
This paper addresses the complex multi-period log-truck routing and scheduling problem (LTRSP) in the forest industry, proposing an enhanced mathematical programming formulation and a decomposition heuristic to solve large-scale instances. The Canadian forest industry faces significant logistical challenges due to vast distances, seasonal variability, market fluctuations, and environmental concerns. Efficient transportation is essential for maintaining both economic viability and environmental sustainability. This research presents a comprehensive framework for routing and scheduling, starting with an analysis of industry rules to design a routing network. A mixed-integer linear programming (MILP) model is then formulated to capture these rules, integrating spatial and temporal constraints. Subsequently, a solving approach, Relax-and-Fix, is applied to historical data provided by a Canadian forest company. The results demonstrate that the framework can generate optimal solutions for daily problems and near-optimal solutions for weekly problems within reasonable computation times. This work ofers an end-to-end framework for tackling LTRSP, developed in collaboration with forest companies and incorporating all their critical business constraints, distinguishing it from existing approaches in the literature.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".