Maximizing the throughput of multimodal logistic platforms by simulation-optimization: The Duferco case study
Why this work is in the frame
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Bibliographic record
Abstract
In this work a multimodal transport platform designed by Duferco near a steel factory in Belgium is considered. Aim of this paper is to present a simulation-optimization decision system for evaluating the maximum throughput of the platform in presence of uncertainties over the durations of the operations. A greedy algorithm and a local search phase has been implemented in order to create a feasible schedule of the planned operations over different time horizons (from a month to a year). Once a schedule has been computed using approximated input data, it is simulated, introducing stochasticity in the system parameters. From the simulation results it is possible to better evaluate some of the scheduling data, modify them accordingly, and re-schedule and simulate the system. This procedure is iteratively applied until an asymptotic state is reached. Extensive computational results show how the new solutions improve the company best practices.
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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 | 0.000 |
| 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 it