Vehicle Routing in Multi-Echelon Distribution Systems with Cross-Docking: A Systematic Lexical-Metanarrative Analysis
Bibliographic record
Abstract
Multi-echelon distribution systems and more precisely, optimization of LTL routes related to them is one of the most popular subjects in the last 5 years of vehicle routing research. Although a plethora of models, methods and visions is found, it is still difficult to compare them because they use different terminologies and some authors insist on the fact there are a multitude of close but different problems. This paper presents the main concepts of multi-echelon distribution with cross-docks and the notation standards for cost optimization in this field on an attempt of unification, in order to provide a guide to researchers and practitioners. A literature review is first presented, in order to list the main problems and methods that are found in the literature. Then, by a hybrid systematic analysis method combining a lexical and a meta-narrative analysis, the main concepts and standards of multi-echelon based vehicle routing optimization problems are presented. A theoretical model as well as a classification of solving methods, both exact and heuristic, is presented. Finally, research paths are proposed to support both scientists and outbound logistics practitioners.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".