A contextual framework for learning routing experiences in last-mile delivery
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a contextual framework for solving the experience-driven traveling salesman problem in last-mile delivery. The objective of the framework is to generate routes similar to historic high-quality ones as classified by operational experts by considering the unstructured and complex features of the last-mile delivery operations. The framework involves learning a transition weight matrix and using it in a TSP solver to generate high quality routes. In order to learn this matrix, we use descriptive analytics to extract and select important features of the high-quality routes from the data. We present a rule-based method using such extracted features. We then introduce a factorization of the transition weight matrix by features, which reduces the dimensions of the information to be learned. In the predictive analytics stage, we develop (1) Score Guided Coordinate Search as a derivative-free optimization algorithm , and (2) label-guided methods inspired by supervised learning algorithms for learning the routing preferences from the data. Any hidden preferences that are not obtained in the descriptive analytics are captured at this stage. Our approach allows us to blend the advantages of different facets of data science in a single collaborative framework, which is effective in generating high-quality solutions for a last-mile delivery problem. We test the efficiency of the methods using a case study based on Amazon Last-Mile Routing Challenge organized in 2021. A preliminary version of our rule-based method received the third place and a $25,000 award in the challenge. In this paper, we improve the learning performance of our previous methods through predictive analytics, while ensuring that the methods are effective, interpretable and flexible. Our best performing algorithm improves the performance of our rule-based method on an out-of-sample testing dataset by more than 23.1%.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it