Simulation-Driven Analysis of Warehouse Operations in Multi-Level Robotic Mobile Fulfillment Systems to Support Decision-Making

In recent years, Robotic Mobile Fulfillment System (RMFS) as part of a third generation of automated parts-to-picker systems has emerged. These systems were developed to meet the growing demands for flexibility and scalability in modern warehouses, which predominantly handle e-commerce orders. By employing mobile robots, RMFSs efficiently process smaller, customized orders, manage a wide range of storage items, and handle the increasing order lines in e-commerce. Despite their advantages, RMFSs have a notable drawback compared to second-generation order picking systems, such as automated storage and retrieval systems, in terms of space utilization. This deficit is crucial for logistics managers and often limits the practical adoption of RMFSs. The use of multi-level RMFSs, e.g., as mezzanine structures, offers the potential to improve space efficiency. However, this approach remains unexplored and is only partially utilized. Multi-level RMFSs pose additional challenges for logistics managers, resulting in decision problems at strategic, tactical, and operational levels. These decision problems include designing the layout of each level, allocating items across multiple levels, and assigning pods and robots within each level. As a result, planning and operating such systems is significantly more complex compared to single-level warehouses. This thesis examines these decision problems and the performance of multi-level RMFSs through three research questions. The resulting studies address, first, optimal algorithms for assigning orders and pods in multi-level RMFSs (Publication 1); second, the analysis of interactions between individual system parameters of a multi-level RMFS and their impact on system performance (Publication 2); and third, the comparison of multi-level RMFSs with traditional order picking systems (Publication 3). The findings provide valuable insights for optimizing RMFSs in dynamic warehouse environments for e-commerce and establish a foundation for future developments in automated warehouse logistics. In order to do this, first a heuristic is created to solve the combined planning problem. Next, key system factors that greatly improve performance and the best design of an RMFS are identified. Finally, the performance and cost-effectiveness of RMFS are compared to traditional order picking systems using numbers.