Robot-aided electric vehicle routing problem with lockers and prime customers prioritization
Bibliographic record
Abstract
Satisfactory and fast customer service is one of the critical parts of last-mile delivery. Companies like Amazon prioritize Prime members with same-day delivery while offering lockers for customer convenience. Additionally, robot-aided Electric Vehicle (EV) delivery is recognized for its cost efficiency and fast service in densely populated areas. Integrating EVs, delivery robots, and lockers, and prioritizing Prime customers can improve efficiency and service responsiveness. This integrated approach offers home delivery by EVs and robots and self-pickup from lockers. Every customer is assigned a prize (profit), with a higher profit associated with the Prime membership. Each EV dispatches robots, with a “dispatch-wait-collect” tactic, to serve the customers, while some customers are allocated to the lockers. This study introduces the Robot-Aided Electric Vehicle Routing Problem with Lockers and Prime Customer Prioritization (REVRP-LPCP), which aims to determine the least-cost routes for EVs and robots, assign customers to lockers, and prioritize prime customers by serving them within a single-period planning horizon. The REVRP-LPCP is formulated using a mixed-integer linear programming model, improving the EV-only-based delivery system by 52.94% and 21.95% in EV route and utilization costs on average. A metaheuristic is introduced, incorporating problem-specific repair and improvement operators to efficiently address large instances of the problem, outperforming Gurobi in 36 large instances by an average of 2.79% in terms of solution quality. Also, our method has identified 44 new best solutions in the related benchmarks. A comprehensive sensitivity analysis is conducted, assessing various scenarios and providing managerial insights.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".