Disruption Management Approaches for Berth Scheduling in Bulk Terminals
Bibliographic record
Abstract
Maritime terminals are complex transportation systems sensitive to several sources of uncertainty. An alteration in the baseline planning, as a consequence of one or more disruptive events, can lead terminals to a lower quality of the service provided. Therefore, in a context where a terminal strives to maintain or increase competitiveness, it is necessary to consider the uncertainty in the planning and define actions capable of efficiently and effectively mitigating disruptive events. This paper addresses berthing operations at bulk terminals, considering the arrival and handling times as stochastic variables. Hybrid approaches (i.e., proactive-reactive) are proposed in order to provide the port terminal with robust planning capable of reducing the impact of disruptive events by defining uncertainty-tolerant schedules and reactive actions capable of restoring the performance of the terminal when disruptive events arise. Finally, the solution approaches are evaluated together with and without the incorporation of buffer-time management. The computational results corroborate the effectiveness of integrating proactive and reactive approaches in order to maximize the performance of the terminal and reduce the penalty costs derived from alterations in the baseline schedule, with the consequent increase in the terminal competitiveness.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".