Product allocation for an automated order picking system in an e-commerce warehouse : A data mining approach
Abstract: Warehouse automation is a measure E-commerce companies can take to get a more streamlined flow through their warehouse. Order picking is the most labor intensive task in a warehouse. By automating the order picking process companies can lower their costs and improve their response times. This thesis studies the A-frame, an automated order picking system, at a large online pharmacy, Apotea AB. An A-frame has dispensing channels on its side and a conveyor belt that runs through the entire machine. Products for an order are ejected from the channels onto the conveyor belt and at the end of the machine they are dropped into a box. The box is then sealed, labeled and sent to the customer. For the automatic flow to function correctly, all orders picked by the A-frame need to be complete orders. Complete orders are orders where there are no products missing. To maximize the throughput of the A-frame, an appropriate product allocation will be required. Due to the vast number of combinations, it is extremely difficult to identify an optimal product allocation. This study has examined three different approaches to the product allocation problem for an A-frame. The first two methods are based on ranking the products depending on their quantities sold. The last method uses association rule learning, which is a machine learning technique for finding interesting patterns in a data set. Association rule learning was used to find which products were associated to each other. These associations were then placed in a graph structure and solved using a heuristic. To evaluate the different allocation methods, a simulation model was created. The A-frame was simulated using a discrete event simulation, which meant all methods could be tested on the same data to correctly compare the performance of each allocation. The study showed that the heuristic using association rules gave the highest number of picks for the tested period. However, it was only marginally better than the method that first removed orders that could not be picked from the A-frame and then ranked all products by their quantities sold. The study's conclusion is that while association rule learning resulted in the highest number of picked orders, the gain of using it does not motivate its complexity. Instead a more simple approach by ranking products by their quantities sold should be used. Warehousing in the era of E-commerce has to be fast, correct and cheap.
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