Two-stage optimization of instant distribution of fresh products based on improved NSGA-III algorithm
Bibliographic record
Abstract
As an important part of the fresh produce business format, fresh food instant delivery encounters numerous challenges. Issues like high losses, complex cold chains and time sensitivity lead to increased costs. Additionally, the living space of end-delivery personnel is under pressure and the talent market is saturated. The platform algorithms focus on the interests of themselves and customers while relatively overlooking those of delivery personnel, which affects the overall operation quality, resulting in a significant reduction in delivery efficiency and a remarkable decline in service quality, and further leading to the loss of user stickiness. Therefore, optimizing the fresh food delivery route and considering the interests of multiple parties to improve efficiency and service quality is a crucial research issue in the field of fresh food instant delivery. This paper designs a three-objective static model for fresh food instant delivery aiming at minimizing the total cost, maximizing customer satisfaction and maximizing riders satisfaction. Considering the dynamic changes of orders during the actual operation process and in combination with the dynamics of newly added orders, a multi-objective dynamic model with the goals of minimizing the total cost, minimizing the average customer dissatisfaction and maximizing the income fairness of riders is further established. Based on the constructed models and by incorporating the SPBO strategy, the NSGA-III algorithm is improved and designed to make it more adaptable to the multi-objective optimization requirements in the fresh food instant delivery scenario. This study selects five operational points within a specific region of a fresh food self-operated platform and the order data from a particular day as research cases to obtain the relevant parameters required for the model and conduct case analysis. Based on the platform's business priorities and development needs, appropriate Pareto solutions are selected. Additionally, the feasibility and effectiveness of the improved algorithm are verified through algorithmic comparison. The research aims to provide valuable references and insightful implications for the management decisions of relevant fresh food self-operated platforms, as well as to continuously optimize the management and service of the instant delivery process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".