Title: Multiple factors collaborative optimisation of intelligent storage system

Authors: Shuhui Bi; Qiuyang Wang; Yuan Xu; Yudong Zhang

Addresses: School of Electrical Engineering, University of Jinan, Jinan, Shandong 250022, China ' School of Electrical Engineering, University of Jinan, Jinan, Shandong 250022, China ' School of Electrical Engineering, University of Jinan, Jinan, Shandong 250022, China ' School of Computing and Mathematical Sciences, University of Leicester, East Midlands, LE1 7RH, UK

Abstract: For improving the storage efficiency of intelligent warehousing systems and avoiding local optimum problem, this paper studies the mutually coordinated strategy for inbound and outbound, which is realised by collaboratively analysing the influence of multiple factors such as order, location and path. Firstly, the correlation between stock keeping units (SKU) in historical orders is analysed by the cosine similarity algorithm, and a storage location optimisation model is established. Then, an improved grey wolf optimiser (IGWO) algorithm integrating genetic algorithm (GA) is proposed, which overcomes the disadvantage of insufficient global search ability from GWO algorithm. Moreover, according to the characteristics of the goods placement, an outbound strategy based on item clustering is put forward. The items in multiple orders are clustered and redistributed based on K-means clustering algorithm, and the improved ant colony optimisation (IACO) is given to solve the path optimisation problem. Finally, the effectiveness of the proposed algorithms is proved by analysing the delivery efficiency under different strategies. It demonstrated that the proposed IGWO is stable in solving the storage space optimisation problems with different SKU numbers, and it improves the algorithm solution speed by 50.2% on average than simulated annealing genetic algorithm (SAGA).

Keywords: cosine similarity algorithm; grey wolf optimiser; GWO; K-means; ant colony optimisation; ACO.

DOI: 10.1504/IJAMECHS.2023.134820

International Journal of Advanced Mechatronic Systems, 2023 Vol.10 No.4, pp.165 - 173

Received: 13 Feb 2023
Accepted: 15 May 2023
Published online: 13 Nov 2023 *

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