Reinforcement Learning for Intermodal Transportation Planning with Time Windows and Limited Cargo Capacity
Bibliographic record
Abstract
This paper addresses the enhancement of practical intermodal transportation efficiency by synergizing train and truck utilization for the seamless movement of freights across diverse geographical locations in a country. The core challenge revolves around determining the optimal allocation of freights, either individually by trucks or collectively with train mode in each location, while accounting for time windows constraints associated with each order and the inherent capacity limitations of the transportation modes. To tackle this complex optimization problem, we employ the renowned Q-Learning reinforcement learning algorithm. This enables the derivation of an optimal dispatch policy predicated on the selection of appropriate transportation modes. In order to establish a robust benchmark for comparison, we introduce three baseline metaheuristic models: Tabu Search, Simulated Annealing, and a hybrid approach merging Tabu Search with Simulated Annealing. Our methodologies undergo rigorous testing using realistic datasets of varying sizes. The outcomes distinctly demonstrate the superior performance of Q-learning over the baseline models. Furthermore, the Q-Learning approach showcases its efficacy in real-time scenario handling, even when confronted with substantial intermodal transportation challenges on a large scale.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".