Linear programming models for optimal workload and batching in pick-and-pass warehousing systems

TL;DR: Optimal workload and batching models for pick-and-pass warehousing systems optimize order picking process and reduce picking time by up to 45%.

Abstract: Pick-and-pass systems are a part of picker-to-parts order-picking systems and constitute a very common storage solution in cases where customer orders are usually small and need to be completed very quickly. As workers pick items in the zones connected by conveyor, their work needs to be coordinated. The paper presents MILP models that optimize the order-picking process. The first model uses information about expected demand for items to solve the storage location problem and balance the workload across zones. The task of the next model is order-batching and sequencing – two concepts are presented that meet different assumptions. The results of the exemplary tasks solved with the use of the proposed MILP models show that the total picking time of a set of orders can be reduced by about 35-45% in comparison with random policies. The paper presents an equation for the lower bound of a makespan. Recommendations about the number of zones that guarantee the required system efficiency are also introduced.