Adaptive large neighbourhood search for the multi-depot arc routing problem with flexible assignment of end depot and different arc types
Bibliographic record
Abstract
This article introduces an advanced solution to optimize street sweeping operations by extending a multi-depot arc routing problem. The key enhancement involves flexible end depot assignments, where vehicles start and conclude shifts at designated depots. A notable constraint requires subsequent shifts to begin from the destination depot of the preceding shift. The problem involves servicing highway exclusively during night shifts, while other arc types can be addressed during both day and night. The objective is to identify optimal shifts meeting practical criteria while adhering to constraints like maximum shift duration. To address this, a mixed-integer linear programming (MILP) model is presented. It aims to minimize the number of shifts and total travel time. Given the computational complexity of large instances, an adaptive large neighbourhood search (ALNS) metaheuristic was developed. This approach incorporates specialized operators that address unique attributes such as arc type and depot assignments, ensuring arcs are repositioned based on their type and proximity to depots. This tailored approach provides a distinct advantage over classical ALNS operators, as numerical tests indicate that the specialized operators are more efficient in comparison. The approach is evaluated on larger and a real-world instances, demonstrating notable performance in solution quality and computational efficiency.
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.
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 | 0.001 | 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".