Robot-aided electric vehicle routing problem with lockers and prime customers prioritization
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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