Open AccessHybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse
Author(s) -
Jianglong Yang,
Li Zhou,
Huwei Liu
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0249543
Subject(s) - rack , order picking , computer science , cluster analysis , genetic algorithm , position (finance) , order (exchange) , algorithm , warehouse , cluster (spacecraft) , basis (linear algebra) , data mining , mathematical optimization , artificial intelligence , engineering , mathematics , machine learning , mechanical engineering , finance , marketing , economics , business , geometry , programming language
The utilization of a storage space can be considerably improved by using dense mobile racks. However, it is necessary to perform an optimisation study on the order picking to reduce the time cost as much as possible. According to the channel location information that needs to be sorted, the multiple orders are divided into different batches by using hierarchical clustering. On this basis, a mathematical model for the virtual order clusters formed in the batches is established to optimize the order cluster picking and rack position movement, with the minimum picking time as the objective. For this model, a hybrid genetic algorithm is designed, and the characteristics of the different examples and solution algorithms are further analysed to provide a reference for the solution of the order picking optimisation problem in a dense mobile rack warehouse.