Insights into the application of the traveling salesman problem to logistics without considering financial risk: A bibliometric study
Bibliographic record
Abstract
Suppliers can use different strategies to distribute their products, Among the most common complex optimization problems related to the transportation of products is the traveling salesman problem. In the traveling-salesman problem, a route is chosen that visits each node exactly once, taking into account the shortest travel time, and finally returns to the original node. In this problem, all nodes must be visited. If we consider the application of this problem in logistics, we can study the necessity of this problem in transportation means such as trucks or drones. The upcoming paper is thoroughly studied and researched considering the related articles published in the last three decades, and bibliometric analysis is used for the details of this problem. This paper aims to statistically evaluate the influence and importance of the traveling salesman on logistics without considering financial risk by presenting an analysis of the works published between 1983 and 2023. As part of our comprehensive literature review table with analysis of export, we will conduct a comprehensive review of the most relevant articles in the field from 2020 to 2023 to better understand the trend in the subject in the last few years. Data were obtained from the Web of Science and focused on metrics such as the total number of publications, citations, average citations per publication, and trending countries. Graphical and statistical analysis was performed using Excel and R-Studio. China, the USA, and Germany are the countries with the most publications. Laporte is the most prolific author with 8 publications. Much research has been done on this topic, especially in the Journal of transportation research part E-logistic with 43 articles, and the main application areas are logistics, vehicles, and drones. These data may prove useful to researchers seeking an overview of the traveling salesman problem to determine future research directions.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.041 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".