Paper Info
Title
Items assignment optimization for complex automated picking Systems
Abstract
Order picking operation is the most expensive and time consuming process of all assignments in distribution center. The high efficient and low consumption automated systems, especially complex automated picking systems have been widely promoted and used. How to improve the efficiency and accuracy of the automated picking system becomes an important issue for improving the business capability of the distribution centers. By analyzing the operating mode of complex automated picking systems, the model of complex automated sorting systems is established and the operation time model is constructed based on the serial order picking strategy with the sequence from right to left. The items assignment optimization models for different types of picking machines are constructed, minimizing total picking time. The improved niche genetic algorithm is designed. In order to improve algorithm convergence speed, the k-means clustering method is used and the results are regarded as the initial population of clustering algorithm. By restricting the number of chromosomes whose location distribution of a certain item is fixed, the niche elimination operation process is improved and the diversity of population is maintained. Through the simulation analysis, the items assignment optimization algorithm leads to 7.5% decrease of the total order picking time, in comparison with the results determined by the traditional EIQ-ABC method. On the basis of items assignment optimization simulation experiments, items configuration and sort comparison tests in a multi-items picking area are designed, again validating the effectiveness.
Year
DOI
Venue
2019
10.1007/s10586-017-1529-5
Cluster Computing
Keywords
DocType
Volume
Complex automated picking systems, Items assignment, Order picking strategy, Niched genetic algorithm
Journal
22
Issue
ISSN
Citations 
Supplement
1573-7543
0
PageRank 
References 
Authors
0.34
4
3
Name
Order
Citations
PageRank
Debao Liu100.34
Xiaofeng Zhao200.34
Yanyan Wang3218.87