Balanced COD-CLARANS: A Constrained Clustering Algorithm to Optimize Logistics Distribution Network

DOI

10.2991/aiie-16.2016.33How to use a DOI?

Keywords

data mining; constrained clustering; logistics distribution network; balance-driven; obstacle

Abstract

In this paper, we focus on the problem of the siting of distribution stations, which is of key importance during designing a logistics distribution network. This problem includes dealing with physical obstacles in real world, making the orders as close as possible to their respective stations and making the workload of each station as balanced as possible. To solve this problem, we use a dataset of the geographical coordinates of all orders of a certain express company per day in Shanghai, and propose an algorithm called Balanced COD-CLARANS, which is a constrained clustering algorithm capable of handling physical obstacles and balance factor and outputting a set of clusters for decision-making. Besides, we design the experiment to prove that Balanced COD-CLARANS works well.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Tong Zhang
AU  - Dong Wang
AU  - Haonan Chen
PY  - 2016/11
DA  - 2016/11
TI  - Balanced COD-CLARANS: A Constrained Clustering Algorithm to Optimize Logistics Distribution Network
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 141
EP  - 145
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.33
DO  - 10.2991/aiie-16.2016.33
ID  - Zhang2016/11
ER  -