Deep Reinforcement Learning for Revenue Management under Uncertainty in Master Stowage Planning on Container Vessels
Bibliographic record
Abstract
Advanced planning policies obtained by machine learning have shown promising<br/>results in solving well-known combinatorial optimization problems in transportation and logistics. However, a significant challenge arises when dealing with complex action spaces in realistic planning, where it is less straightforward for machine learning models to generate feasible actions. A relevant and complex example is master stowage planning on container vessels, which plays a crucial role in global trade and the green transition. This planning problem aims to maximize cargo revenue and minimize operational costs while addressing strict constraints and demand uncertainty. To tackle this challenge, our paper introduces a deep reinforcement learning framework with a general feasibility layer to solve a novel Markov decision process of master stowage planning under demand uncertainty. The experimental evaluation shows that our architecture efficiently finds feasible solutions for a multistage stochastic optimization problem, which is intractable using traditional benchmark methods from combinatorial optimization. Our approach demonstrates the potential of advanced planning policies to tackle complex, real-world problems, with implications for global trade and sustainability.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".