AI-powered Optimization Framework for Inter-connected Warehouse Inventory Rebalancing
DescriptionInter-connected warehouse network, which allows inventory inter-transshipment to ensure fast delivery and timely inventory rebalancing, plays an ever-present role in supply chain management, regional planning, and many other disciplines. For instance, perishable food retailers need to transship items to ensure product supply and to avoid product waste. Bike sharing system operators need to redistribute bikes from full stations to empty stations for a high service level. It is challenging for these practitioners to timely rebalance the level of inventory with shortages or overages due to the dynamics of product demand at different locations and the limited operation cost. This proposal will develop an optimization framework by integrating machine learning predictive analytics and spatial-temporal based clustering algorithms to facilitate the inventory rebalancing operations in an inter-connected warehouse network. Solid experiments will be conducted to validate the effectiveness and efficiency of our research solutions using real-world data from perishable product retailer and bike sharing systems.
|Effective start/end date||1/08/19 → …|