Designing Distribution Networks: Formulations and Solution Heuristic
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".