Designing Distribution Networks: Formulations and Solution Heuristic
Why this work is in the frame
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Bibliographic record
Abstract
The fast development of transport activities and the introduction of shipment consolidation have considerably changed the logistics context over the last three decades. Consolidation terminals, also called transshipment centers (TC) or hubs, have justified their presence by improving the loading of trucks in terms of both volume and weight. In addition, the possibility of using external carriers, exclusively or in coordination with a private fleet, can reduce costs and increase customer service. The right combination of these strategies can dramatically impact the cost of transport. However, the complexity of the decisions has also increased and existing models have to be improved to tackle these new challenges. In this paper, after discussing the different formulations for distribution networks with transshipment centers existing in the literature, we present a new model and an efficient metaheuristic that determines the number and the location of TCs as well as the best transportation alternative—LTL, FTL, Parcel, or own fleet—on each segment accounting for both weight and volume metrics. The ability of our heuristic to solve this complex problem comes from a judicious combination of tabu search and variable neighborhood search. The performance of this approach is evaluated on several test data problems generated with real cost structures published by a U.S. carrier. The heuristic solutions are compared to optimal ones obtained by an exact method for small-sized instances of the simpler problems. Finally, we address issues in carrier price structure to achieve efficient shipment 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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