An improved adaptive large neighborhood search algorithm on collaborative last mile delivery with roaming customers
Bibliographic record
Abstract
This paper addresses the challenge of rising operational costs in last-mile delivery caused by end-customer no-shows. The study proposes a collaborative operational framework for last-mile delivery that accommodates roaming customers, enabling them to be serviced by multiple depots as they transition between different locations. A mixed-integer programming (MIP) model is formulated to minimize the operational costs of last-mile delivery under the proposed framework. To improve the model’s practicality and computational efficiency, an adaptive large neighborhood search (ALNS) algorithm is developed, incorporating tailored neighborhood structures. Furthermore, a late acceptance strategy is embedded within the algorithm to mitigate the risk of premature convergence to local optima. The experimental results demonstrate that, in the absence of depot collaboration, the multi-depot model achieves a 16.9% reduction in operational costs compared to the single-depot model. Moreover, when depot collaboration is enabled, the average cost reduction percentage significantly increases to 40.37%. Notably, under the multi-depot collaborative framework, considering customers' roaming behavior—as opposed to fixed single-location assumptions—leads to a substantial 54.6% reduction in operational costs.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".