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

Conference proceedings article

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Prachitmutita, Issaret; Padungweang, Praisan; Rojanapornpun, Olarn;

ผู้เผยแพร่: Hindawi

ปีที่เผยแพร่ (ค.ศ.): 2020

ISBN: 9781450377591

นอก: 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

ภาษา: English-Great Britain (EN-GB)

ดูบนเว็บไซต์ของสำนักพิมพ์

บทคัดย่อ

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.

คำสำคัญ

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