Research on the Optimization of Goods Location Based on Greedy Algorithm

DOI

10.2991/essaeme-17.2017.369How to use a DOI?

Keywords

goods location optimization, picking cost, greedy algorithm

Abstract

With the rapid development of China's economy, the competition between enterprises is increasing day by day, and it is difficult to form a strong competitive advantage by the advantages of product quality, product type and product cycle. The logistics service for the purpose of cost saving is becoming the world's "Third profit source". In terms of logistics costs, the cost of warehousing and transportation is often the largest. At present, most Chinese enterprises have their own warehouses. Therefore, it is relatively easy to solve the warehouse cost. Reasonable use of the warehouse is also one of the key means to save the cost of inventory. This article mainly aims at the optimization of the item's position, and chooses the sorting method to optimize the goods for the small batch and multi-frequency items, mainly represented by the auto parts. Based on the general principle of cargo location optimization, the optimal solution of the model is obtained by establishing the mathematical model of cargo location optimization, given the constraint condition, comparing the relevant solution method and combining with the actual needs, and choosing the greedy algorithm to solve the problem. The best program, to achieve cost savings, to achieve the purpose of maximizing the interests of enterprises, has a certain practical significance.

Copyright

© 2017, 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  - Hongmei Ju
AU  - Qiqi Liu
PY  - 2017/07
DA  - 2017/07
TI  - Research on the Optimization of Goods Location Based on Greedy Algorithm
BT  - Proceedings of the 2017 3rd International Conference on Economics, Social Science, Arts, Education and Management Engineering (ESSAEME 2017)
PB  - Atlantis Press
SN  - 2352-5398
UR  - https://doi.org/10.2991/essaeme-17.2017.369
DO  - 10.2991/essaeme-17.2017.369
ID  - Ju2017/07
ER  -