Correlated assignment strategy can improve any order-picking operation
Industrial Engineering
Clustering techniques for stock location and order-picking in a distribution
Computers and Operations Research - Special issue on aggregation and disaggregation in operations research
Batching orders in warehouses by minimizing travel distance with genetic algorithms
Computers in Industry - Special issue: Application of genetics algorithms in industry
Computers and Industrial Engineering
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Warehouse management is currently facing fierce competition. By integrating information systems, retailers order more frequently with multiple items, but each order has smaller quantities. The situation becomes more stressful in a disintermediation supply-demand system. A good example is in the Business-to-Customer (B2C) online retailing business in which warehouses have to fulfill divergence orders directly. This study proposes a two-stage Clustering-Assignment Problem Model (CAPM) for the customized-orders picking problem. For multi-item-small-quantity orders, the CAPM targets a between-item association rather than the traditional group clustering to reduce the picking distance. The first stage of CAPM draws item association indices, based on between-item support, from customers' orders. It then develops a mathematical programming model to search for the maximum total item support. The second stage applies assignment techniques to locate the clustered group in the storage place so as to minimize picking distance. We use Lingo commercial software to help the solution-finding procedures. By emphasizing the item association, CAPM is suitable for orders with multiple items and smaller quantities in the modern retailing sector. It also more effectively shortens the picking distance compared with popular frequency-based and random assignment storage methods. In the example of the drug distribution center studied herein, CAPM proves more effective as it reduces over 45% of the picking distances versus the current set-up.