Work

Hybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse

Year: 2021

Type: article

Abstract: 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.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0249543

Authors: Jianglong Yang, Li Zhou, Huwei Liu

Institutions: Capital University of Economics and Business, Beijing Wuzi University

Language: English

Topic: Advanced Manufacturing and Logistics Optimization

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,749

Funders: National Natural Science Foundation of China, Beijing Intelligent Logistics System Collaborative Innovation Center