Models and Algorithms for E-Commerce Fulfillment Network Design
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Greening, Lacy M.
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Abstract
Large e-retailers today must manage complex fulfillment networks to ship purchased products directly to customers. Large firms may be able to generate substantial cost savings by building consolidated loads with many shipments outbound from some stocking locations into other facilities and potentially transferring those shipments into subsequent consolidated loads prior to last-mile delivery. Such a system of consolidated loads is a private middle-mile network, and the design of these networks is the focus of this thesis.
In Chapter 2, we study a middle-mile network design optimization problem with fixed origins and destinations to build load consolidation plans that minimize cost and satisfy customer shipment lead-time constraints. We propose models that extend traditional flat network service network design problems to capture waiting delays between load dispatches and ensure that shipment lead-time requirements are satisfied with a desired probability. In Chapter 3, we propose an approach that leverages data on customer purchasing sensitivity to quoted order-to-delivery times (ODTs) when designing middle-mile consolidation networks to maximize the profit of e-commerce retailers. Results from a U.S.-based e-commerce partner show that our approach leads to a profit increase of 10% when simply allowing a marginal change of one day to the current ODT quotes.
In Chapters 2 and 3, we develop effective integer-programming-based large neighborhood search approaches that identify strong primal solutions to integer programming models for middle-mile network design; however, assessing the quality of solutions is difficult given weak dual bounds. Thus, Chapter 4 is motivated by the desire to improve exact optimization approaches by developing bound-improving methods for middle-mile consolidation network design problems. In this chapter, we begin this work by studying the single-mode problem of Chapter 2 without waiting delay considerations, more generally known as a multicommodity capacitated network design (MCND) problem with unsplittable demand. We propose a path-based formulation which utilizes a multiple-choice selection of binary variables to assign arc capacities and study a structured relaxation of this formulation to define two new classes of valid inequalities that produce stronger linear programming relaxation solutions as compared to previous work.
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2024-05-17
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Dissertation