Multi-depot heterogeneous fleet vehicle routing problem with time windows: Airline and roadway integrated routing
Bibliographic record
Abstract
In transportation, the multi-depot heterogeneous fleet vehicle routing problem with time windows (MDHFVRPTW) is one of the hard-to-solve real-life problems. In the study, a new node-based MDHFVRPTW has been developed. Unlike other studies in the literature, heterogeneous fleets including both airline and roadway vehicles are used for routing. In the model, real-life data of the airline and roadway are taken into consideration. In particular, important aviation constraints such as the range of the aircraft, landing and take-off cycle (LTO) cost according to the engine type, and the penalty cost are presented in the model. The problem is analysed by using narrow and wide time windows, which is the realization of fast and normal demand. A new hybrid genetic algorithm with variable neighborhood search (HGA-VNS) has been proposed for the solution of the MDHFVRPTW model. In the solution of the model, remarkable results have been obtained with the HGA-VNS algorithm compared to the genetic algorithm and off-the-shelf solvers. Also, the HGA-VNS algorithm has been tested with small and large-scale instances and compared with other studies in the literature. It is thought that the proposed MDHFVRPTW model and the developed HGA-VNS algorithm will bring a different perspective to transportation.
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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 | 0.000 |
| 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) | 0.002 | 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".