A Comparison of Destination Clustering using Density-based Algorithm on the Trip Planning Optimization for Last-Mile Parcel Delivery

Conference proceedings article

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Publication Details

Author list: Prachitmutita, Issaret; Padungweang, Praisan; Rojanapornpun, Olarn;

Publisher: Hindawi

Publication year: 2020

ISBN: 9781450377591

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089174185&doi=10.1145%2f3406601.3406641&partnerID=40&md5=3014140937a6276fceeef2b920946c02

Languages: English-Great Britain (EN-GB)

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Abstract

This study examines the pipeline for real-world delivery trip planning optimization. The study focuses on the capacitated vehicle routing problems. Density-based clustering algorithms were applied prior to resolving the capacitated vehicle routing problem (CVRP) to reduce processing time and to maintain acceptable efficiency. The experimental results with 25 CVRP pipelines were compared. The results showed that the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) method achieved the highest performance. It could reduce the time of trip planning by 30-40% and total distance by 2.2% compared with the traditional method. © 2020 ACM.

Keywords

Capacitated Vehicle Routing Problem, Clustering, Last-Mile Delivery, Logistics, Route Optimization