Date on Master's Thesis/Doctoral Dissertation


Document Type

Doctoral Dissertation

Degree Name

Ph. D.


Industrial Engineering

Degree Program

Industrial Engineering, PhD

Committee Chair

Gue, Kevin

Committee Co-Chair (if applicable)

Usher, John

Committee Member

Usher, John

Committee Member

DePuy, Gail

Committee Member

Nasraoui, Olfa

Author's Keywords

warehouse optimization; SKU assignment; slot allocation; forward reserve problem; multimode forward area; predictive analytics


The forward area is a small area of a warehouse with a low picking cost. Two approaches that are investigated for selecting the SKUs of this area and the allocated space are the static and the dynamic approaches. In the case that decisions about the forward area are made periodically (e.g. yearly) and the products' demand patterns are completely ignored, the FRP is static. We developed two heuristics that solve the large discrete assignment, allocation, and sizing problem simultaneously. Replenishing the same product in the same place of the forward area brings about a ``Locked" layout of the fast picking area during the planning horizon. By using a dynamic slotting approach, the product pick locations within the warehouse are allowed to change and pick operations can accommodate the variability in the product demand pattern. A dynamic approach can introduce the latest fast movers to the forward area, as an opportunity arises. The primary objective of this dissertation is to formally define the dynamic FRP. One main mission of this research is to define a generic dynamic slotting problem while also demonstrating the strengths of this approach over the static model. Dynamic slotting continuously adjusts the current state of the forward area with real-time decisions in conjunction with demand predictive analytics. Applying different order data with different demand volatility, we show that the dynamic model always outperforms the static model. The benefits attained from the dynamic model over the static model are greater for more volatile warehouses.