An optimization algorithm based on text clustering for warehouse storage location allocation

With the rapid development of e-commerce and logistics, people want to increase the efficiency of order picking in warehouses to meet the growing volume of orders. Optimizing the distribution of items in the warehouse location is one of the main ways to improve the efficiency of order picking. Some researchers have carried out correlation analysis to optimize the allocation of storage space in the case of strong correlation among order items. In the case of weak correlation among items, the method of pure correlation analysis is no longer applicable. In response to this situation, this paper proposes an algorithm based on text clustering and correlation analysis. In the case of weak correlation among items, this algorithm can optimize warehouse storage location allocation, aiming to reduce the total distance of order picking operations. In this paper, the real business history orders is used to simulate the experiment. By comparing with two other commonly-used storage location allocation algorithms, the allocation of the storage location determined by the algorithm proposed in this paper reduces significantly the total distance of the order picking operation.

[1]  Steve Uhlig,et al.  Design and Evaluation of the Optimal Cache Allocation for Content-Centric Networking , 2016, IEEE Transactions on Computers.

[2]  P. Quigley,et al.  Effect of a group-based exercise program on balance in elderly , 2007, Clinical interventions in aging.

[3]  S. Vijayarani,et al.  Comparative analysis of association rule mining algorithms , 2016, 2016 International Conference on Inventive Computation Technologies (ICICT).

[4]  Alex Alves Freitas,et al.  Probabilistic Clustering for Hierarchical Multi-Label Classification of Protein Functions , 2013, ECML/PKDD.

[5]  Harpreet Singh,et al.  Performance evaluation of a novel hybrid clustering algorithm using birch and K-means , 2015, 2015 Annual IEEE India Conference (INDICON).

[6]  Ruibin Bai,et al.  Cold chain configuration design: location-allocation decision-making using coordination, value deterioration, and big data approximation , 2018, Annals of Operations Research.

[7]  Yongping Huang,et al.  A Text Classification Algorithm Based on Rocchio and Hierarchical Clustering , 2011, ICIC.

[8]  Nagamma Patil,et al.  Recommender system based on Hierarchical Clustering algorithm Chameleon , 2015, 2015 IEEE International Advance Computing Conference (IACC).

[9]  Zhang Ce,et al.  Improved Chameleon Algorithm , 2009 .

[10]  Richard D. Keane,et al.  Theory of cross-correlation analysis of PIV images , 1992 .

[11]  Tadeusz Morzy,et al.  Scalable Hierarchical Clustering Method for Sequences of Categorical Values , 2001, PAKDD.

[12]  Yasunari Tamada,et al.  Allocation of Decision-Making Authority with Principal's Reputation Concerns , 2005 .

[13]  Kees Jan Roodbergen,et al.  Routing order pickers in a warehouse with a middle aisle , 2001, Eur. J. Oper. Res..

[14]  Madan Gopal,et al.  A comparison study on multiple binary-class SVM methods for unilabel text categorization , 2010, Pattern Recognit. Lett..

[15]  Cheng Wei,et al.  Improved CURE algorithm and application of clustering for large-scale data , 2011, 2011 IEEE International Symposium on IT in Medicine and Education.

[16]  Enrico Motta,et al.  A library of problem-solving components based on the integration of the search paradigm with task and method ontologies , 1998, Int. J. Hum. Comput. Stud..

[17]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.