Conference Agenda

Sequential zone picking is a strategy to pick items from a warehouse. It is often used if the number of items per order is comparably low, the overall number of items is low and the volume of orders is high. If there are really few different items compared to the order volume, the same item is placed in multiple zones to improve performance. This gives rise to the problem of order routing, which determines for every order where to pick the required items.

In this presentation, we introduce a storage assignment algorithm for sequential zone picking, which is specifically designed for small product assortments by assigning the same SKUs into multiple picking zones. Our algorithm anticipates the necessary routing decision to balance the zone workload efficiently. Based on this approach, we compare three different strategies on how to deal with distinct assortments in a multi-segmented zone picking system.

Today many activities in operations management are automated, but humans still play an important role due to their flexibility. Human-machine interactions need to be managed efficiently to ensure smooth operations. In the standard human-machine interaction at the shop-floor level, machines determine the assignment of tasks, while human workers mainly execute these often repetitive and monotonous tasks.

One downside of such work division is the mental impoverishment of workers, which relates to stagnating productivity and low job satisfaction. We draw on self-determination theory to argue that enabling workers to self-select tasks can resolve these issues. We conduct a field study in a semi-automated warehouse to test if task self-selection improves workers' job performance and job perceptions, compared to automatic task assignment by the machine.

Indeed, we observe an 8.7% increase in workers' job performance; a great result considering the former year-long stagnating productivity. Interestingly, we also observe lower job satisfaction, when “pickers could pick their picks”. Follow-up interviews with pickers, shift leaders, and managers revealed that the new system of task self-selection suspended workers' informal work arrangements.

Our study touches upon an important issue in operations management: integrating human-machine interaction into the workplace while ensuring the job does not get too mundane for workers. We discuss the difficulties that may arise while implementing such systems, and show their potential to improve worker productivity.

Order picking in warehouses is well known to be one of the most cost-intensive parts of order fulfillment in warehouses, especially in a picker-to-parts setting, while offering vast optimization potential. Joint Order Selection, Allocation, Batching, and Picking is a unique picker-to-parts order picking setting recently proposed by researchers from Zalando. The problem captures constraints and decision processes observed in large-scale warehouses of online retailers. It combines several of the usually disjoint decision layers with a unique goal of achieving an item throughput goal, thus relaxing the usual strict constraint of serving all orders. Due to the typically large problem instances coupled with strong runtime requirements, existing picker-to-parts approaches are not well suited for this setting. We build upon the work of Khan et al. and propose and evaluate several new heuristics, which improve upon the existing algorithms and provide adjustable runtime to performance trade-offs. We further discuss managerial insights obtained from the heuristics’ performances.

E-commerce and omnichannel retailing are significantly changing the requirements for ware-houses and order picking. Warehouses must store an increasing variety of stock keeping units (SKUs), while order picking systems (OPSs) must handle higher order volumes of ever smaller orders within continuously shrinking picking and delivery time windows. Literature indicates that these requirements are difficult to achieve in standard OPSs, and instead identifies zone picking systems (ZPSs) as a promising alternative. Zone picking is the partitioning of a picking area into multiple non-overlapping zones, with each zone assigned to an order picker responsible for retrieving the corresponding part of an order. Following from a systematic literature review on zone picking, this presentation describes present variations of ZPSs as well as their strengths and weaknesses. It further illustrates order picking problems regarding ZPSs and presents opportunities for future research, e.g., the inclusion of human factors such as physical fatigue in order picking problems.