Clustering-Based Enhanced Ant Colony Optimization for Multi-Trip Vehicle Routing Problem with Heterogeneous Fleet and Time Windows: An Industrial Case Study
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces a novel approach to solving a practical variant of the Vehicle Routing Problem (VRP), the multi-trip VRP with heterogeneous fleet and time windows (MTVRPHFTW). The approach integrates an improved Ant Colony Optimization (IACO) metaheuristic, a modified density-based spatial clustering of application with noise (DBSCAN-Plus) clustering, and a Micro-Cluster Fusion Scheme. The proposed framework aims to optimize vehicle routes by minimizing total travelling distance and time while ensuring a fair distribution of workload among the vehicles (drivers). To evaluate the proposed algorithm, referred to as the Ant Colony Optimization (ACO) algorithm with improvement mechanisms (Cluster Improved Ant Colony Optimization, CIACO), real-world data from a logistics company in Canada was utilized. This empirical testing aims to validate the algorithm's effectiveness in practical applications. The experimental results of CIACO demonstrate that the proposed algorithm outperforms existing methods in terms of reducing traveling distance, minimizing traveling time, optimizing the use of smaller vehicles to reduce CO2 emissions, achieving balanced workloads among drivers, and improving overall route optimization.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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