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Zhai, M.; Wang, Z. Rack Locations in the Mobile-Rack Picking System. Encyclopedia. Available online: (accessed on 16 April 2024).
Zhai M, Wang Z. Rack Locations in the Mobile-Rack Picking System. Encyclopedia. Available at: Accessed April 16, 2024.
Zhai, Mengyue, Zheng Wang. "Rack Locations in the Mobile-Rack Picking System" Encyclopedia, (accessed April 16, 2024).
Zhai, M., & Wang, Z. (2024, February 23). Rack Locations in the Mobile-Rack Picking System. In Encyclopedia.
Zhai, Mengyue and Zheng Wang. "Rack Locations in the Mobile-Rack Picking System." Encyclopedia. Web. 23 February, 2024.
Rack Locations in the Mobile-Rack Picking System

The flexible movement of racks in the mobile-rack picking system (MRPS) significantly improves the picking efficiency of e-commerce orders with the characteristics of “one order multi–items” and creates a challenging problem of how to place racks in the warehouse. This is because the placement of each rack in the MRPS directly influences the distance that racks need to be moved during order picking, which in turn affects the order picking efficiency.

mobile-rack picking system rack location optimization degrees of rack heat and relevance

1. Introduction

The rapid development of electronic commerce in recent years has posed substantial implications and challenges for warehouse operation management. Given the demanding timeliness and large-scale, diverse, small-batch nature of customer orders, the urgent resolution of the problem of implementing precise and efficient order picking operations is a critical concern for every warehouse enterprise [1][2]. The MRPS, operating as a semi-automated picking system, provides a fresh way to address this problem [3]. This system deviates from the traditional fixed rack storage system as its racks are smaller in size and can be moved to any desired position within the warehouse. The picking operation is executed by the robot, which carries the rack to the picking station, thereby implementing the “parts to picker” mode. Instead of being stored in a fixed location like a traditional warehouse, each Stock Keeping Unit (SKU) can be scattered and put on numerous mobile racks, each rack holding varying amounts of different categories of SKUs [4][5]. The MRPS significantly improves the flexibility of SKUs storage and efficiently addresses the difficulty of e-commerce order picking by allowing for the relocation of racks in the warehouse [6]. As a result, it is highly desired by numerous e-commerce warehousing enterprises. Presently, e-commerce giants such as JD, Walmart, and Alibaba have embraced MRPS [7].
Nevertheless, this system also gives rise to the challenge of optimizing rack location. The positioning of each rack in the MRPS directly affects the distance traveled by the robots during order picking, thereby impacting the efficiency of the order picking. How can the arrangement of the rack in the warehouse be periodically adjusted, taking into account the regularity of customer orders and the categories and quantities of SKUs stored on the rack, with the objective of minimizing the travel distance of robots? This is a challenging problem that every MRPS decision-maker faces, especially when dealing with e-commerce order picking, in order to maximize efficiency and leverage the system’s advantages. This problem is accompanied by the emergence of MRPS, which restricts the development of a new generation of order picking systems. It is different from the location optimization problem in the fixed rack storage system, its solution surpasses the applicability of existing theoretical methods, and its NP-Hard characteristics significantly amplify the problem’s difficulty, especially when addressing large-size customer orders [8][9].

2. Rack Locations in the Mobile-Rack Picking System

In terms of storage assignment, the traditional manual order picking system solely requires determining the storage position of the SKUs on the rack. This is because the racks have fixed storage locations, and pickers walk in the channels to retrieve SKUs from the racks. In other words, it is a problem of assigning storage locations to SKUs [10]. Hausman et al. were the first to compare three storage assignment strategies: random assignment, full turnover-based assignment, and class-based turnover assignment [11]. Subsequently, Kuo et al. assigned the storage locations with the objective of increasing warehouse utilization and customer satisfaction by reducing customer waiting time [12]. Glock and Grosse studied the problem of storage assignment with the lowest warehouse picking cost [13], while Bortolini et al. optimized SKUs storage locations based on the best rack stability [14]. The objective-setting methods of the above studies are relatively common. In addition, some scholars optimized SKUs storage locations by analyzing certain attributes of the products themselves: Caron et al. proposed a storage strategy based on the Cube-per-Order Index (COI) [15]. Li and Nof proposed a storage optimization method to classify the SKUs based on their attributes [16]. Yang and Nguyen developed a constrained clustering method integrated with principal component analysis to meet the need of clustering stored SKUs with the consideration of practical storage constraints [17]. Since there is a certain correlation between different SKUs in practice and they are often purchased at the same time, other scholars take this correlation into consideration: Pan et al. used the frequency of simultaneous purchases of SKUs to quantify the correlation between products [18]. Xiao and Zheng proposed to classify all SKUs based on correlation and then match them with classified storage locations [19]. Jane and Laih proposed to classify SKUs according to association relationships, and similar SKUs are randomly placed in the same area [20].
The above research focuses on the problem of storage assignment under the traditional “picker-to-parts” model. Within the MRPS system, the robot transports the racks from the storage area to the picking station using the “parts-to-picker” picking mode. Meanwhile, the pickers stand still at the picking station, retrieving the SKUs from the racks to fulfill the orders’ picking. After picking is completed, the robot needs to transport the racks from the picking station back to the empty location for storage [21]. Therefore, the storage assignment problem of MRPS can be divided into SKUs storage racks optimization and rack location optimization. Some scholars have used a two-stage solution idea to study the storage assignment problem of MRPS. In the first stage, correlation analysis methods and clustering methods are used to perform correlation clustering on SKUs, so that SKUs that are often purchased at the same time can be stored on the same rack, and to realize that the SKUs stored on racks are maximized in overall relevance. In the second stage, Li et al. proposed a new scattering storage strategy based on rack turnover rate, aiming to decentralize the storage of racks with higher turnover rates to avoid congestion and accumulation of a large number of AGVs in high-frequency operating areas at the same time [22]. Yuan et al. considered rack turnover rate, relevance between racks, and work balance in the lanes to construct an optimization model for minimizing the total travel distance of robots and designed a hybrid algorithm that combines a greedy algorithm and improved simulated annealing [23]. The above research considered both the two sub-problems of SKUs storage optimization and rack location optimization. However, in the real operation of the MRPS, the storage assignment of SKUs on the rack and the adjustment of the rack position are two types of work with different frequencies at different times, so they belong to two different levels of the problem.
Considering that the decision of rack storage location has an important impact on the moving distance of the rack and the workload of the robot, many scholars have conducted research on the RLOP of RMFS in recent years. Weidinger et al. formalized the rack location problem as a special interval scheduling problem by calculating the movement distances between racks and picking stations and introduced a mathematical approach based on adaptive large neighborhood search to solve the problem. It is shown that a simple rack location strategy like the shortest path storage can gain better results when the objective is to minimize the distance between the racks and the picking stations [8]. Yuan et al. developed a fluid model to analyze the performance of velocity-based storage policies. They characterized the maximum possible improvement from applying a velocity-based storage policy in comparison to the random storage policy. The result showed that class-based storage with two or three classes can achieve most of the potential benefits and that these benefits increase with greater variation in the rack velocities [5]. Merschformann et al. introduced several rack repositioning policies such as random policy, fixed policy, and nearest policy [24]. Cai et al. integrated the rack location assignment problem with the path planning decisions to minimize the total travel time of robots [25]. Zhuang et al. investigated the rack storage and robot assignment to racks problem during order processing. They formulated this problem with the objective of minimizing the makespan of this system and developed a matheuristic decomposition approach based on a rolling horizon framework and the simulated annealing method. In the above study, the storage location of the rack is constantly changing, and the rack may be assigned to a new storage location for each completed pick. Merschformann et al. demonstrated that employing variable storage locations is advantageous in numerous scenarios compared to a fixed strategy. This approach effectively minimizes the traveling time of robots, enabling them to promptly accomplish subsequent tasks. However, this strategy may result in no longer useful racks being stored in very prominent storage locations, which is likely to cause problems with congestion and further robot travel distances during order picking later. Moreover, assigning a new storage location after each rack use during the order picking process will greatly increase the computational burden of the intelligent operating system [26][27].
In actual situations, customers have increasingly higher requirements on the delivery time and cost of their orders. Owing to the impact of variables like sales, seasons, product life cycles, and turnover rates, rack locations in MRPS must be changed on a regular basis in order to accommodate these changes and satisfy customer demands.


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