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Optimizing the order picking operations is indispensable for warehouses that promise a high customer service level. While many areas for improvement have been identified and studied in the literature, a large gap remains between academia and practice. To help with closing this gap, we perform a case‐study in collaboration with a spare‐parts warehouse in Belgium. In this study, we optimize the order picking operations of the company, using the actual warehouse layout and real order data. A state‐of‐the‐art online integrated order batching, picker routing and batch scheduling algorithm is adapted to consider multiple real‐life constraints. More specifically, the dynamic arrival of new orders is considered, and a capacity constraint on the sorting installation should be respected. Furthermore, a new waiting strategy is studied in which order pickers can temporarily postpone certain orders, as combining them with possible future order arrivals may allow for more efficient overall picking performance. Finally, the performance of the current operating policy is compared with that of both a seed batching heuristic and our metaheuristic algorithm by use of an ANOVA analysis. The results indicate that the number of order pickers can be reduced by 12.5% if the new optimization algorithm is used, accompanied by an improvement in the offered customer service level. [ABSTRACT FROM AUTHOR]

Although technological innovation has enabled a new wave of warehouse automation, human involvement remains necessary for most order picking operations in grocery retailing. This has spawned new forms of interaction between humans, machines, and intelligent software, that is, cyber‐sociotechnical systems. However, scant empirical field‐based research has been conducted on how this transition impacts human learning and the perception of work characteristics. Considering that humans are an essential element of these systems, it is fundamentally important to quantify the impact of these transformations when aspiring to improve performance, quality, and workers' well‐being as primary outcomes of order picking systems. This study utilized a mixed‐methods design, developing and applying parametric and non‐parametric approaches to operationalize learning progress, and semi‐structured interviews were conducted to examine perceived work characteristics. The findings indicate that the perception–cognition–motor–action cycle for learning by doing tasks can be accelerated through real‐time feedback provided by the order picking system. Furthermore, perceived work autonomy and feedback from the picking system are constant or perceived as greater when human decisions are accepted. The results have valuable implications for logistics practitioners, emphasizing the need for human‐centered work system design. [ABSTRACT FROM AUTHOR]

Put‐to‐light order picking systems invert the basic logic of conventional picker‐to‐parts systems. Instead of successively visiting the storage positions of the stock keeping units (SKUs) when collecting picking orders, an order picker accompanies successive bins each containing multiple items of a specific SKU along a lane of subsequent orders. Whenever the picker passes an order requiring the current SKU, which is indicated by a light signal, she puts the requested number of items into the bin associated with the order. Such an order picking system is well‐suited if the assortment is not overly large and all orders demand similar SKUs, so that it is mainly applied in distribution centers of brick‐and‐mortar retail chains. This paper evaluates four different setups of put‐to‐light systems, which, during operations, require the solution of different storage assignment and SKU sequencing problems. We formulate these problems, prove computational complexity, and suggest suited solution algorithms. By applying these algorithms in a comprehensive computational study, we benchmark the impact of the four different setups on picking performance. In this way, warehouse managers receive decision support on how to set up their put‐to‐light systems. [ABSTRACT FROM AUTHOR]

This paper gives an overview of recent research on the performance evaluation and design of carousel systems. We discuss picking strategies for problems involving one carousel, consider the throughput of the system for problems involving two carousels, give an overview of related problems in this area and present an extensive literature review. Emphasis has been given on future research directions in this area. [ABSTRACT FROM AUTHOR]

Order picking is one of the most important process steps in logistics. Because of their flexibility, human beings cannot be replaced by machines. But if workers, in order, picking systems are equipped with a head-mounted display, Augmented Reality can improve the information visualization. In this paper, the development of such a system—called Pick-by-Vision—is presented. The system is evaluated in a user study performed in a real storage environment. Important logistics figures as well as the subjective strain were measured. The results show that a Pick-by-Vision system can considerably improve industrial order picking processes. [ABSTRACT FROM AUTHOR]

The article discusses the importance of the discrete order picking technologies in business enterprises. Discrete order picking is the most simple and reliable kind of picking orders which commonly used in the operations of the electronic commerce. It has the ability to deliver order information to the pickers through mobile units, as well as to locate depots and lay out products.