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An efficient data-driven method for storage location assignment under item correlation considerations
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AI
DOI：10.1080/23302674.2023.2228447.png)
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摘要
En 中文
This research investigates the storage location assignment problems of correlated-items under a realistic multiple-cross-aisle warehouse setting. To accommodate the shortest picking route decision and update customer orders in each replenishment cycle to capture the changing trend in customer preferences. An item-correlations considered fitness function is developed to evaluate the benefit of exchanging item locations and minimise the picking costs. A data-driven storage location assignment method called storage location assignment for correlated-item method is proposed to improve the order picking efficiency. The explicit considerations make this work distinct from existing studies: (1) correlation among items in customer orders, (2) penalty for crossing-aisles in warehouse traffic, and (3) real retail dataset adopted. Our method considers the effect of correlated items in customer orders, through a storage exchange benefit function to evaluate the fitness of storage location to minimise warehouse operation costs and enhance operation efficiency. With a real ecommerce dataset, the numerical study results show that our method can reduce the travelling distance by 5-10% compared with a conventional turnover-based storage policy. Our method not only outperforms in terms of travel distance. The picking time improvement is even more significant for large warehouse if a moderate penalty for crossing-aisles is considered.
Keyword:
Data-driven
item correlation
storage location assignment
order picking
cross-aisle penalty
期刊
I
IF:
4.6
论文数:
1.1K
被引数:
7.3K
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