Solving the Open Route Capacitated Periodic Vehicle Routing Problem With Time Windows to Service Microgrids Powered by Uncertain Renewable Generation
Bibliographic record
Abstract
Abstract Supply Chain and logistics operations are high-cost operations that require planning to keep costs minimum. One of the most crucial supply chain stages is the distribution stage, including delivering products to retailers and/or customers. The problem of products’ distribution consumes considerable amounts of time and money and is modeled in many situations as a Vehicle Routing Problem (VRP). In this paper, servicing and provision of power transactions to an electric micro-grid using a fleet of mobile energy storage systems (MESSs) during a specific time horizon is targeted using the developed model for the well-known Capacitated VRP with Time Windows (CVRPTW). The process of servicing power transactions of an electric micro-grid is repeated for each station periodically, which results in a problem known as periodic VRP (PVRP). The MESSs in the developed model do not necessarily return back to their original trip starting point (depot); hence, the problem at hand is considered as Open-Route as well. Therefore, the Open Route Capacitated Periodic VRP with Time Windows (OCPVRPTW) is studied in this paper. The model is formulated and solved using commercial software to obtain exact solutions to the tackled problem instances. For future work and due to the combinatorial nature of the problem, Ant Colony Optimization will be used to solve larger instances of the problem.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".