Urban Distribution Optimization Based on Order Clustering and Customer Classification

DOI

10.2991/smont-19.2019.31How to use a DOI?

Keywords

large-scale urban distribution; clustering analysis; customer priority; vehicle routing problem d

Abstract

With the improvement of the consumption structure and the increase of consumption levels, the market size of urban distribution is gradually increasing. In response to the problem of large-scale urban distribution vehicle routing problem, a two-stage algorithm of “first partitioning and then scheduling” is proposed. First, the k-means algorithm is used to partition the scale orders according to the order attributes, and then the particle swarm algorithm is used to do the optimization of vehicle routes for the orders in each area; at the same time, the difference in urgency of demands of different customers is considered, and a weight of each customer is introduced to express the priority. Finally, by using D distribution company in Beijing as an example and through programming, the optimal distribution scheduling scheme is obtained. The distribution cost is reduced by 32% and the loading rate is increased by 4%, which verifies the effectiveness and feasibility of the algorithm.

Copyright

© 2019, 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/).

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TY  - CONF
AU  - Rui Guo
PY  - 2019/04
DA  - 2019/04
TI  - Urban Distribution Optimization Based on Order Clustering and Customer Classification
BT  - Proceedings of the 2019 International Conference on Modeling, Simulation, Optimization and Numerical Techniques (SMONT 2019)
PB  - Atlantis Press
SP  - 140
EP  - 144
SN  - 1951-6851
UR  - https://doi.org/10.2991/smont-19.2019.31
DO  - 10.2991/smont-19.2019.31
ID  - Guo2019/04
ER  -