Beurteilung unterschiedlicher Use Case-Konfigurationen in einem Robotic Mobile Fulfilment System

In recent years, hybrid order picking systems like Robotic Mobile Fulfilment Systems (RMFS) have become established and widely used in ecommerce. Companies from other logistics areas with different use cases often decide against investing in RMFS due to high investment risks or unknown performance benefits. This work contains a performance evaluation of three different use case configurations based on logistics areas in e-commerce and production conducted by a simulation model for multi-level RMFS with an integrated rolling planning approach. The model leads to a demonstrator supporting logistics managers in their decisionmaking. Those and other users can vary input parameters in the demonstrator, create different use case configurations, and run the simulation model to evaluate performance by key performance indicators (KPIs). The work depends on several discussions and interviews with logistics experts to define realistic use cases the logistics manager can identify

A Study of Layout Determination of Mobile Rack Warehouse

Labor cost reduction and resourceful space utilization are essential for efficient warehouse operation. In these challenges, mobile rack technology can increase storage space by 75% or more. The mobile racking system is a system where racks are constructed on a mobile base and steered by rails on the floor. Driven by an electrical motor, the mobile base moves along the rails to open one or more access aisles. Moving rack technology eliminates aisle space between racks and increases storage space by synchronizing wheels or rails. Hence, companies can stock and access a high volume of products while enduring space-efficient. Minimizing the cost per pallet is especially critical for cooler and freezer facilities. However, there is no straightforward solution to determining the optimal layout of a mobile rack warehouse. When designing a mobile rack warehouse, you can increase storage space by a minimum of aisles, but it can reduce work efficiency at the same time. Therefore, it is necessary to determine the appropriate layout of mobile racks considering both work efficiency and space efficiency. This study proposes a practical layout of a mobile rack warehouse to harmonize work and space efficiency. To validate the model, we examine several numerical examples and analyze the warehouse area, the total amount of rack movement, and working hours. Depending on the operating cost, a different layout is suggested

Order picking optimization with order assignment and multiple workstations in KIVA warehouses

We consider the problem of allocating orders and racks to multiple stations and sequencing their interlinked processing flows at each station in the robot-assisted KIVA warehouse. The various decisions involved in the problem, which are closely associated and must be solved in real time, are often tackled separately for ease of treatment. However, exploiting the synergy between order assignment and picking station scheduling benefits picking efficiency. We develop a comprehensive mathematical model that takes the synergy into consideration to minimize the total number of rack visits. To solve this intractable problem, we develop an efficient algorithm based on simulated annealing and dynamic programming. Computational studies show that the proposed approach outperforms the rule-based policies used in practice in terms of solution quality. Moreover, the results reveal that ignoring the order assignment policy leads to considerable optimality gaps for real-world-sized instances

Velocity-based Storage Assignment in Semi-automated Storage Systems

Our research focuses on the storage decision in a semi-automated storage system, where the inventory is stored on mobile storage pods. In a typical system, each storage pod carries a mixture of items, and the inventory of each item is spread over multiple storage pods. These pods are transported by robotic drives to stationary stations on the boundary of the storage zone where associates conduct pick or stow operations. The storage decision is to decide to which storage location within the storage zone to return a pod upon the completion of a pick or stow operation. The storage decision has a direct impact on the total travel time, and hence the workload of the robotic drives. We develop a fluid model to analyze the performance of velocity-based storage policies. We characterize the maximum possible improvement from applying a velocity-based storage policy in comparison to the random storage policy. We show 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 pod velocities. To validate the findings, we build a discrete-time simulator with real industry data. We observe an 8% to 10% reduction in the travel distance with a 2-class or 3-class storage policy, depending on the parameter settings. From a sensitivity analysis we establish the robustness of the class-based storage policies as they continue to perform well under a broad range of warehouse settings including different zoning strategies, resource utilization and space utilization levels