In robotic mobile fulfillment systems, which are warehousing technologies that follow the parts-to-picker concept, the order picking process involves two decisions of how to schedule the processing of orders and of how to sequence the racks that are lifted and transported by robots to the picking station in order to supply the requested items. We propose a heuristic solution approach for solving the order-scheduling and rack-sequencing problem at a single picking station. Our approach utilizes column generation to partition the set of orders into batches. The goal is to minimize the number of rack assignments to these batches, which minimizes the rack-visits. The generated batches possess a specific property that allows for the straightforward derivation of an order-processing schedule and rack sequence. To further improve the solution, we refine the heuristic approach by rearranging the processing of batches and their assigned racks. We conducted a comprehensive and comparative computational. The method outperforms several other heuristics in terms of both solution quality and runtime on the majority of instances. Additionally, our heuristic yields satisfactory results when embedded into a framework designed to solve the problem across multiple picking stations, particularly for small-case data.

In order to provide fast access times to the stored items in warehouses, different types of storage systems exist. A number of factors, including the physical size and weight of the items to be stored, the frequency of use, and the resources (such as space) available, will determine the type of system that is suitable for a particular warehouse. In this talk, we will examine a new type of storage system that has received little attention from the scientific community so far, the compact storage systems. Compact storage systems aim at achieving the highest possible space utilization rate given limited storage space.

We want to shed light on the specifics of such storage systems and analyze how to optimize the storage and retrieval of items within them (e.g., by order picking and batching, the sequencing of the retrievals, and control, navigation, and relocation of storage units). In this talk, we formulate combinatorial optimization problems addressing the retrieval of items, discuss algorithmic approaches and their computational complexity.

Delivery of parcels in urban environments can be conducted in various ways. Next to traditional van delivery, cargo bikes are in use already today. In the future, also robots or drones may be used for this purpose. Each of those systems comes with certain (dis-)advantages regarding capacity, speed, environmental friendliness, and compatibility with customer preferences. The availability of these alternative transportation systems therefore raises the question, which of them to apply in a particular city environment and how to combine complementary systems in order to benefit from their mutual advantages. In order to investigate this, we propose a mathematical optimization model for the operations management of two-tier urban parcel logistics, which can handle alternative modes of transportation in isolation but also in combination. We propose an Adaptive Large Neighborhood Search for solving this optimization model and present computational results that shed light on the performance of the heuristic as well as the suitability of combined fleets of vans, bikes, robots and/or drones in urban environments. Through this, we can draw recommendations on which technologies to consider when implementing innovative parcel delivery systems for urban environments.