Sentiment Analysis for Driver Selection in Fuzzy Capacitated Vehicle Routing Problem With Simultaneous Pick-Up and Drop in Shared Transportation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Shared transportation involves vehicles, drivers, and customers, the interactions among which could have potential long-term impacts on the business. Machine learning techniques, and their integration with existing models, have proved to significantly improve results. Availability of extensive unstructured textual data has fostered research in text generation and mining. Cognizance and analysis of such data has become crucial for modern commercial applications. Thus, in this article, sentiment analysis, using natural language processing, is used to quantify raw customer feedback, to obtain drivers' ratings and perform driver selection. Selection of the best drivers for ferrying riders is desired and modeled accordingly. An integrated vehicle routing problem with generalized fuzzy travel durations, and uncertain pick-up and drop demands, is modeled and solved using a hybrid genetic algorithm. Fuzzy simulations in a credibilistic environment are employed to evaluate the cost function. Performance of selected drivers is used to update driver ratings for the subsequent run, and the process is repeated multiple times. The results obtained authenticate the purpose of this article, and comparative analysis is performed to further corroborate the model's capability. An additional case of triangular fuzzy ratings is also illustrated, and its impact on the model discussed. Suggestions for driver classification are also provided for personnel management.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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