Teun Van Gils

Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review

Warehouses deliver labor-intensive services to customers. Underperformance may result in high costs and unsatisfied customer demand. New market developments force warehouses to handle a large number of orders within tight time windows. To cope with this, order picking operations need to be optimized by solving a wide range of planning problems. Optimizing order picking planning problems sequentially may yield a suboptimal overall warehouse performance. Still, previous warehouse planning reviews focus on individual planning problems. This literature review differs by investigating combinations of multiple order picking planning problems. A state-of-the-art review and classification of the scientific literature investigating combinations of tactical and operational order picking planning problems in picker-to-parts systems is presented with the aim of determining how planning problems are related. Furthermore, this literature review aims to find excellent policy combinations and to provide guidelines how warehouse managers can benefit from combining planning problems, in order to design efficient order picking systems and improve customer service. Combining multiple order picking planning problems results in substantial efficiency benefits, which are required to face new market developments.

The use of time series forecasting in zone order picking systems to predict order pickers’ workload

In order to differentiate from competitors in terms of customer service, warehouses accept late orders while providing delivery in a quick and timely way. This trend leads to a reduced time to pick an order. This paper introduces workload forecasting in a warehouse context, in particular a zone picking warehouse. Improved workforce planning can contribute to an effective and efficient order picking process. Most order picking publications treat demand as known in advance. As warehouses accept late orders, the assumption of a constant given demand is questioned in this paper. The objective of this study is to present time series forecasting models that perform well in a zone picking warehouse. A real-life case study demonstrates the value of applying time series forecasting models to forecast the daily number of order lines. The forecast of order lines, along with order pickers’ productivity, can be used by warehouse supervisors to determine the daily required number of order pickers, as well as the allocation of order pickers across warehouse zones. Time series are applied on an aggregated level, as well as on a disaggregated zone level. Both bottom-up and top-down approaches are evaluated in order to find the best-performing forecasting method.

Improving Order Picking Efficiency by Analyzing Combinations of Storage, Batching, Zoning, and Routing Policies

In order to differentiate from competitors in terms of customer service, warehouses accept late orders while providing delivery in a quick and timely way. This trend leads to a reduced time to pick an order. The objective of this research is to simulate and evaluate the interaction between several storage, batching, zone picking and routing policies in order to reduce the order picker travel distance. The value of integrating these four operation policy decisions is proven by a real-life case study. A full factorial ANOVA provides insight into the interactions between storage, batching, zoning, and routing policies. The results of the study clearly indicate that warehouses can achieve significant benefits by considering storage, batching, zone picking, and routing policies simultaneously. Awareness of the influence of an individual policy decision on the overall warehouse performance is required to manage warehouse operations, resulting in enhanced customer service.