Title:
Demand Management and Delivery Optimization for E-Retail Fulfillment
Demand Management and Delivery Optimization for E-Retail Fulfillment
Author(s)
Banerjee, Dipayan
Advisor(s)
Erera, Alan L.
Toriello, Alejandro
Toriello, Alejandro
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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 solve the optimization problem of maximizing the area of a single-vehicle delivery zone as a function of the zone’s distance from the depot. We then demonstrate how to derive fleet sizes from these maximum areas and propose an associated Voronoi approach to partition the 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 model proposed by Stroh (2021), we first derive new bounds on the optimization 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. 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 customer satisfaction and profit. 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. We analyze a class of linear threshold policies proposed by Hodge (2008), in which each online (i.e., less desirable) customer is accepted if and only if the remaining inventory at the customer's arrival time exceeds a threshold that linearly decreases over the selling horizon, to show that these policies achieve uniformly bounded regret.
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Date Issued
2024-04-24
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Resource Type
Text
Resource Subtype
Dissertation