Optimization of direct transshipment scheduling for river–sea intermodal transport with vessel arrival time matching
Bibliographic record
Abstract
Dry bulk river–sea intermodal transport is a critical consideration when connecting inland waterways and oceanic shipping, yet its efficiency hinges on precise vessel arrival time matching. The challenge of vessel arrival time matching has been exacerbated by existing research gaps. Current studies often focus on single vessel types or static scenarios, lacking integrated optimization of dynamic coordination between sea-going and river vessels, and failing to unify time and cost objectives. To address this, we develop a multiobjective scheduling model incorporating real-time arrival data from the dry bulk river–sea intermodal information platform to minimize total port time and operational costs. A heuristic genetic algorithm with adaptive weight adjustment (λ) is designed, achieving convergence within 200 iterations and a solution time of 33 seconds. This algorithm is validated under balanced conditions (λ=0.5) and is shown to yield 108.53 hours of total port time and 278,165.2 yuan in operational costs. Sensitivity analysis reveals a significant tradeoff: λ is reduced from 0.9 to 0.1, leading to an increase in port time by 1.42% but a reduction in costs of 3.03%. This reflects an improved flexibility in cost optimization as a result of resource manipulability. In contrast, port time is constrained by physical limits, such as loading/unloading efficiency. The framework developed provides practical decisional support for ports, with higher λ values (0.7–0.9) enabling rapid turnover in congestion and lower values (0.1–0.3) prioritizing cost economy. Future work should extend this approach to stochastic environments and incorporate multistakeholder coordination using game theory approaches.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".