Parametric design of time-sensitive routing with recipient-dependent contributions
Bibliographic record
Abstract
Last-mile delivery complexities intensify for perishable goods, which must maintain quality, mainly when transported on non-refrigerated vehicles. If recipients are unavailable, delivery failure may prolong the delivery of perishable goods, thus jeopardizing their integrity. This study proposes recipient-dependent last-mile delivery solutions for perishable goods with time-sensitive delivery routes where the recipients contribute to the process. We explore applications of Autonomous Vehicles (AVs) in recipient-dependent deliveries of perishable goods and compare traditional truck delivery with a proposed AV pickup policy and other multi-echelon routing policies. We propose a parametric design of the policies, characterizing each policy by a set of variables inspired by the network design literature. In this study, routes are regarded as length-constrained, which is essential for the time-sensitive delivery of perishable goods. We compare the optimal cost of policies in length-bounding (time-sensitive) with capacity-bounding routes. A detailed dominance space analysis highlights the optimal policy under various cost structures and shows that the status quo for truck delivery is dominated as the number of deliveries increases. Increasing hand-off costs also lead to the dominance of AV and hybrid policies over traditional truck delivery. We validate the proposed managerial insights through a case study of a Walmart location delivery service in Toronto, proving the applicability of the models. This research contributes to the strategic integration of AVs in last-mile delivery of perishable goods.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".