Research on Service Quality Improvement of Takeaway Platform Based on Artificial Intelligence
Bibliographic record
Abstract
Service quality is the key for takeaway platforms to maintain their advantages in the ierce market competition.In this study, we construct a mathematical model to solve the takeaway delivery problem by ant colony algorithm, so as to realize the takeaway delivery path planning based on ant colony algorithm.The grey neural network model is used to predict the order demand in the takeaway platform, and the fruit ly algorithm is used to ine-tune and optimize the parameters in the grey neural network model to avoid the model from falling into the local optimum and to improve the accuracy of the model in predicting the takeaway demand.Through simulation experiments, it is found that the planning algorithm in this paper can successfully realize the reasonable planning of takeaway delivery paths when the initial positions of merchants, users and delivery workers are known.The gray neural network optimized using the fruit ly algorithm is also able to accurately predict the takeout demand of platform users based on the order data provided by the takeout platform.Using the method of this paper for the improvement of the service quality of the takeaway platform can signi icantly improve the delivery ef iciency of takeaway orders and develop personalized service strategies according to user demand, thus enhancing user satisfaction with the takeaway platform.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".