A Decomposition-Based Framework for Large-Scale Multi-Period Log-Truck Routing and Scheduling: A Case Study in Canadian Forestry
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it