Improving order-picking operation through efficient storage location assignment: A new approach

•Order-picking efficiency is improved via efficient storage assignment.•New concept is introduced for better assignment by exploiting order characteristics.•A data-based approach (DBA) is developed for the solution.•DBA outputs competitive results on real data with short running time.•DBA is extende...

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Veröffentlicht in:Computers & industrial engineering 2020-01, Vol.139, p.106186, Article 106186
Hauptverfasser: Wang, Meng, Zhang, Ren-Qian, Fan, Kun
Format: Artikel
Sprache:eng
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Zusammenfassung:•Order-picking efficiency is improved via efficient storage assignment.•New concept is introduced for better assignment by exploiting order characteristics.•A data-based approach (DBA) is developed for the solution.•DBA outputs competitive results on real data with short running time.•DBA is extended to the case of high-level warehouses. Fast development of online retail industry requires customer orders to be fulfilled within tight windows, where order-picking, the most time-consuming and labor-intensive activity in warehouses, plays an important role. One of the basic ways to improve order-picking operation is assigning storage locations to appropriate items. The storage location assignment problem is in general NP-hard and is mainly solved by heuristics which usually suffer from limited solution quality or high computational effort, especially for large scale problems. In literature, most studies make the storage assignment decisions according to item properties, such as turnover or correlation, which are statistically extracted from item orders. These storage methods follow a data→concept→assign decision mechanism and may ignore useful data characteristics that are not conceptualized. This paper presents a new approach to improve the order-picking operation, which directly uses item orders to make the decisions without any statistical treatments, i.e., following a data→assign mechanism. The concept of good move pair is introduced to quickly find a better assignment through directly exploiting data characteristics of item orders, and an iterative algorithm is developed to minimize the total travel distance. We evaluate the algorithm on real data and numerical instances, and compare its performance with extant methods in the literature. The results show that the proposed method significantly outperforms other methods in most cases. We also extend the algorithm to the case of high-level warehouses and examine its effectiveness.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2019.106186