Title: Demand Management and Delivery Optimization for E-Retail Fulfillment

Committee:

Dr. Alan Erera (co-advisor), Industrial and Systems Engineering, Georgia Institute of Technology

Dr. Alejandro Toriello (co-advisor), Industrial and Systems Engineering, Georgia Institute of Technology

Dr. Santanu Dey, Industrial and Systems Engineering, Georgia Institute of Technology

Dr. Karen Smilowitz, Industrial Engineering and Management Sciences, Northwestern University

Dr. He Wang, Industrial and Systems Engineering, Georgia Institute of Technology

Date and Time: Thursday, April 11th, 10-11:30 AM ET

Location: Groseclose 226A (Georgia Freight Bureau Conference Room)

Virtual Link: https://gatech.zoom.us/j/98636068379?pwd=MGR4TEQvZFoycHhxR0d0KzZ0bVUzUT09

Abstract:

Increased competition and customer expectations in the e-retail sector have led to the proliferation of rapid fulfillment guarantees, such as same-day delivery (SDD) and next-day delivery (NDD). This thesis studies a series of tactical and operational logistics problems that arise in the design and management of rapid e-retail fulfillment systems.

In Chapter 2, we study the linked tactical design problems of fleet sizing and partitioning a given service region into vehicle routing zones for SDD systems. Using continuous approximations to capture average-case operational behavior, we first consider the optimization problem of independently maximizing the area of a single-vehicle delivery zone. We characterize area-maximizing dispatching policies and leverage these results to develop a procedure for calculating optimal areas as a function of a zone's distance from the depot, given a maximum number of daily dispatches per vehicle. We then demonstrate how to derive fleet sizes from optimal area functions and propose an associated Voronoi approach to partition the service region into single-vehicle zones. 

In Chapter 3, we study the tactical problem of choosing the SDD service region itself – allowing the region to vary over the course of the day – with the objective of maximizing the average number of daily orders served. Using a continuous approximation optimization model proposed by Stroh (2021), we first derive new bounds on the model's objective and variables under a variety of conditions. Then, we illustrate how the theoretical model can be applied to real-world road networks by proposing an iterative method for empirically estimating a single Beardwood-Halton-Hammersley routing constant when service regions vary over time. We use this method to compute a series of SDD system designs in the Phoenix metropolitan area.

In Chapter 4, we study a system in which a common delivery fleet provides service to both SDD and NDD orders placed by e-retail customers who are sensitive to delivery prices. We develop a continuous approximation model of the system and optimize with respect to two separate objectives. We first optimize for customer satisfaction by maximizing the quantity of NDD orders fulfilled one day early given fixed prices. Next, we optimize for total profit; we optimize for a single SDD price, and we then set SDD prices in a two-level scheme with discounts for early-ordering customers. 

In Chapter 5, motivated by multichannel retailers using in-store inventory to satisfy both in-store customers and online rapid delivery requests, we study the finite-horizon continuous-time dynamic yield management problem with stationary arrival rates and two customer classes. We consider a class of linear threshold policies proposed by Hodge (2008). Using a discrete-time Markov chain model, we show that a range of such linear threshold policies achieve uniformly bounded regret. We then generalize this result to analogous policies for the same problem with arbitrarily many customer classes.