| With the development of logistic and information techniques, both responsiveness andefficiency of warehouse systems have been improved. New warehouses employ real-timepicking, dynamic storage, dynamic distribution and stochastic routing systems. Existingwarehouse stochastic modelling literature is usually based on classical probability models orbasic queueing models, and cannot handle the new challenges rising from real-lifewarehouses. On the other hand, stochastic models and theory have evidently developed in thelast 20 years. While stochastic models are potentially efficient tools for warehouse research,the application of these methods in warehouse research is limited. This thesis will bridge thisgap and apply state-of-the-art stochastic models and theory to new-emerging warehousesystems.I will demonstrate the effective application of stochastic models and analysis to complexwarehouse systems, and present four research examples as follows. Firstly, I modelwarehouse real-time processing systems by stochastic polling models in a parallel-aislewarehouse, and a carousel system with capacity constraints. Secondly, I propose to researchwarehouse dynamic storage and putaway systems by queueing network models. Thirdly, Iintend to examine warehouse storage space allocation and revenue management systems bystatic and dynamic stochastic knapsack models. Finally, I want to explore optimal batchingsize and optimal zoning problems in a parallel-aisle warehouse via stochastic integer models. |
