A Vehicle Routing Problem With Option for Outsourcing and Time-Dependent Travel Time
Bibliographic record
Abstract
This paper studies the time-dependent vehicle routing problem with private fleet and common carriers (TDVRPPC), which provides an option to outsource the customer requests and considers real-world time-dependent travel times. The problem is commonly seen in the transportation and logistics industries as it considers the impact of changing traffic conditions on travel times, maximum working hour regulations as well as vehicle capacity constraints. The time-dependent travel time is modeled as a piecewise linear function, based on which a mixed integer programming model is proposed for the TDVRPPC. To solve this NP-hard problem, we customize a hybrid algorithm to harness an adaptive large neighborhood search algorithm for exploration and a tabu search for the exploitation of the search. Through constraint relaxation, dynamic and coordinated adjustment of diversification and intensification strengths of the hybridized procedures, as well as an effective segment-based evaluation method, the proposed algorithm performs well on newly generated test instances for the TDVRPPC and on benchmark instances for the simplified vehicle routing problem with private fleet and common carriers (VRPPC).
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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 | 0.002 |
| 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 | 0.001 |
| 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".