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Carvalhosa, M. A. , Pereira, M.T., Pereira, M. G. , e Oliveira, E.  & Ramos, F.R. (2024). Enhancing last-mile delivery: A hybrid approach with machine learning techniques that captures drivers' knowledge. International Journal of Logistics Research and Applications. N/A

M. A. Carvalhosa et al.,  "Enhancing last-mile delivery: A hybrid approach with machine learning techniques that captures drivers' knowledge", in Int. Journal of Logistics Research and Applications, vol. N/A, 2024

@article{carvalhosa2024_1766352094486,
	author = "Carvalhosa, M. A.  and Pereira, M.T. and Pereira, M. G.  and e Oliveira, E.  and Ramos, F.R.",
	title = "Enhancing last-mile delivery: A hybrid approach with machine learning techniques that captures drivers' knowledge",
	journal = "International Journal of Logistics Research and Applications",
	year = "2024",
	volume = "N/A",
	number = "",
	doi = "10.1080/13675567.2024.2436392",
	url = "https://www.tandfonline.com/journals/cjol20"
}

TY  - JOUR
TI  - Enhancing last-mile delivery: A hybrid approach with machine learning techniques that captures drivers' knowledge
T2  - International Journal of Logistics Research and Applications
VL  - N/A
AU  - Carvalhosa, M. A. 
AU  - Pereira, M.T.
AU  - Pereira, M. G. 
AU  - e Oliveira, E. 
AU  - Ramos, F.R.
PY  - 2024
SN  - 1367-5567
DO  - 10.1080/13675567.2024.2436392
UR  - https://www.tandfonline.com/journals/cjol20
AB  - The rise of e-commerce has transformed last-mile delivery, with companies’ prioritising faster, more flexible options and implementing innovations such as route optimisation. Efficient last-mile delivery is now critical to customer satisfaction and business success. This work aims to bridge the gap between planned and actual delivery routes, a challenge highlighted by the 2021 Amazon Last-Mile Routing Research Challenge. The solution uses a sophisticated hybrid approach, combining machine learning algorithms with automated hyperparameter optimisation. Instead of focusing on individual stops, it predicts sequences of zones. The process involves data pre-processing, a Prediction by a Partial Matching algorithm to identify optimal zone combinations, a Rollout Algorithm to compute zone sequences for unexplored routes, and a Lin-Kernighan-Helsgaun solver for zone-to-zone routing. These steps are seamlessly integrated into a repeatable pipeline that automates hyperparameter fine-tuning. The results obtained indicate a robust solution capable of producing high-quality predictions.
ER  -