Aggregation of orders in distribution centers using data mining

Authors:
Mu-Chen Chen;Cheng-Lung Huang;Kai-Ying Chen;Hsiao-Pin Wu
Affiliations:
Department of Business Management, Institute of Commerce Automation and Management, National Taipei University of Technology, 1, Section 3, Chung-Hsiao E. Road, Taipei 106, Taiwan, ROC;Department of Information Management, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan, ROC;Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan, ROC;Information System Department, Chinese Petroleum Corporation, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2005

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Abstract

This paper considers the problem of constructing order batches for distribution centers using a data mining technique. With the advent of supply chain management, distribution centers fulfill a strategic role of achieving the logistics objectives of shorter cycle times, lower inventories, lower costs and better customer service. Many companies consider both their cost effectiveness and market proficiency to depend primarily on efficient logistics management. Warehouse management system (WMS) presently is considered a key to strengthening company logistics. Order picking is routine in distribution centers. Before picking a large set of orders, effectively grouping orders into batches can accelerate product movement within the storage zone. The order batching procedure has to be implemented in WMS and may be run online many times daily. The literature has proposed numerous batching heuristics for minimizing travel distance or travel time. This paper presents a clustering procedure for an order batching problem in a distribution center with a parallel-aisle layout. A data mining technique of association rule mining is adopted to develop the order clustering approach. Performance comparisons between the developed approach and existing heuristics are given for various problems.