AI-Driven Route Optimization and Sustainable Logistics
Bibliographic record
Abstract
This chapter examines how generative AI helps the food SCM evolve and aims to improve its efficiency, robustness, and sustainability performance. Using generative AI models, predictive analytics, and digital technologies, the paper identifies the AI's usage, data availability, digital supply chain, and the accuracy of the demand forecast. Therefore, the structured quantitative approach was adopted, and responses were collected on a seven-point Likert scale from 215 participants: financial analysts, market specialists, and other supply chain practitioners. Simple descriptive statistics such as frequency distributions and measures of central tendencies and variability were used as data analysis tools, and hypothesis testing involved basic bivariate correlation coefficients and bootstrapping to make the results conservative and replicable. The study points out how applying AI enhances decision-making, cuts costs and wastes, and can also enhance logistics. It also provides valuable information on developing sustainable and flexible food supply chains in complex contexts.
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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 | 0.000 | 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 | 0.001 |
| 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 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".