CLUSTERING AND MULTI-OBJECTIVE OPTIMIZATION OF DECENTRALIZED LAST-MILE DELIVERY

Yuliia LESHCHENKO; Maria YUKHIMCHUK

doi:10.31891/2219-9365-2025-83-9

CLUSTERING AND MULTI-OBJECTIVE OPTIMIZATION OF DECENTRALIZED LAST-MILE DELIVERY

Authors

Yuliia LESHCHENKO Vinnytsia National Technical University https://orcid.org/0009-0003-1563-5949
Maria YUKHIMCHUK Vinnytsia National Technical University https://orcid.org/0000-0002-8131-9739

DOI:

https://doi.org/10.31891/2219-9365-2025-83-9

Keywords:

last mile, decentralized delivery, clustering, route optimization, artificial intelligence, multi-criteria optimization

Abstract

Decentralized last-mile delivery using local hubs reduces transportation time, costs, and CO₂ emissions. The article formulates a multi-criteria mathematical model for route optimization, taking into account time windows and transportation constraints. The use of clustering methods and metaheuristics is proposed to reduce computational complexity and increase efficiency. The results confirm that the combination of geospatial analysis and intelligent algorithms provides a significant increase in the speed, reliability, and environmental sustainability of urban logistics systems.

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Published

2025-08-28

How to Cite

LESHCHENKO, Y., & YUKHIMCHUK, M. (2025). CLUSTERING AND MULTI-OBJECTIVE OPTIMIZATION OF DECENTRALIZED LAST-MILE DELIVERY. MEASURING AND COMPUTING DEVICES IN TECHNOLOGICAL PROCESSES, (3), 67–70. https://doi.org/10.31891/2219-9365-2025-83-9