The stock location assignment problem is concerned and a strategy for prestructuring components and information for the picking work in storehouses is described. This classification is derived from the product structures transformed to support a holistic perception aimed at the material handler/picker. The results imply a more efficient material handling through reorganizing the components in the storage system, in order to support the work from the picker's point of view. A storage location assignment strategy emanating from the product structure is proposed, and is exemplified by using empirical data from a case study. The result from this case study was a reduction in picking information of more than 75%, which in turn greatly reduced the amount of information to the picker.

Storage Location Assignment: Using the Product Structure to Reduce Order Picking Times

The forgotten sons: Warehousing systems for brick-and-mortar retail chains

Order allocation, rack allocation and rack sequencing for pickers in a mobile rack environment

Order picking is one of the most demanding activities in many warehouses in terms of capital and labor. In parts-to-picker systems, automated vehicles or cranes bring the parts to a human picker. The storage assignment policy, the assignment of products to the storage locations, influences order picking efficiency. Commonly used storage assignment policies, such as full turnover-based and class-based storage, only consider the frequency at which each product has been requested but ignore information on the frequency at which products are ordered jointly, known as product affinity. Warehouses can use product affinity to make informed decisions and assign multiple correlated products to the same inventory “pod” to reduce retrieval time. Existing affinity-based assignments sequentially cluster products with high affinity and assign the clusters to storage locations. We propose an integrated cluster allocation (ICA) policy to minimize the retrieval time of parts-to-picker systems based on both product turnover and affinity obtained from historical customer orders. We formulate a mathematical model that can solve small instances and develop a greedy construction heuristic for solving large instances. The ICA storage policy can reduce total retrieval time by up to 40% compared to full turnover-based storage and class-based policies. The model is validated using a real warehouse dataset and tested against uncertainties in customer demand and for different travel time models.

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The impact of integrated cluster-based storage allocation on parts-to-picker warehouse performance

A growing population and increasing real estate costs in many urbanized areas have made space for roomy warehouses with single-deep storage and wide aisles scarce and expensive. Mobile rack warehouses increase the space utilization by providing only a few open aisles at a time for accessing the racks. Whenever a stock keeping unit (SKU) is to be retrieved, neighboring racks mounted on rail tracks have to be moved aside by a strong engine, so that the adjacent aisle opens and the SKU can be accessed. As moving the heavy racks takes considerable time, the resulting waiting time determines large parts of the picking effort. It is, thus, advantageous to sequence picking orders, such that the last aisle visited for the preceding order is also the first aisle to enter when retrieving a subsequent picking order. We formalize the resulting picking order sequencing problem and present suited exact and heuristic solution procedures. These algorithms are tested in a comprehensive computational study and then applied to explore managerial aspects, such as the influence of the number of open aisles on the picking effort.

Sequencing of picking orders in mobile rack warehouses

In response to the scarce space in many urban areas, high-density storage has become a widely discussed topic in warehousing research and practice. We investigate a storage setting where mobile rac...

Amir Foroughi

Papers

High-density storage with mobile racks: Picker routing and product location