Completed Project
Independent Research Project
Interpretable Learning-Augmented Last-Mile Delivery Routing
An interpretable, learning-augmented approach to last-mile delivery routing that combines operations research with interpretable machine learning and hierarchical zone-preference modelling, evaluated on real Amazon last-mile delivery routes using the official challenge scoring.
- Dates
- November 2025 – December 2025
Research Problem
Last-mile delivery routes learned purely from historical data can be accurate but opaque. This project develops an interpretable, learning-augmented routing approach that combines operations research with interpretable machine learning and hierarchical zone-preference modelling, evaluated against real delivery behaviour using the official challenge scoring.
Methodology
Operations ResearchInterpretable Machine LearningRoute OptimisationHierarchical Zone-Preference ModellingChronological Train–Validation–Test SplittingOfficial Challenge ScoringSensitivity AnalysisRoute-Level Robustness Analysis
Tools
Python
Verified Data
- Analysed 6,112 Amazon last-mile delivery routes
- Evaluated on 926 unseen test routes
Verified Results
- Reduced aggregate median official route score by 39.9% compared with nearest-neighbour routing
- Improved 88.1% of test routes
- Limited median travel-time increase to 4.83%
- Reduced median zone re-entries from 21 to 0
Current Stage
Completed independent research project