Stochastic Models for Order Picking Systems

In this thesis, we develop new stochastic models for the performance evaluation of several highly state-of-the-art warehousing systems that, in particular, adequately describe and predict the consequences of variability in, e.g. order arrivals, and picking times on the performance of a warehousing system. Stochastic models provide an indispensable tool for this task and have already proved to be extremely valuable for areas such as manufacturing, communication, and computer systems. Also for warehousing systems, the stochastic models provide valuable guidance in the rapid comparison of key features of different design alternatives and allow operations to be optimized in order to meet prespecified performance targets.