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Abstract

This dissertation focuses on the control of service and manufacturing systems through the two levers of pricing and matching. Chapters 1 and 2 focus on the ride-hailing industry. Chapter 3 studies a make-to-stock manufacturing system. In Chapter 1, we study how spatial pricing and search friction can impact the taxi market in New York City. Our model captures the interplay between spatial pricing, where prices depend on either the origin of the ride alone or both its origin and destination, and search friction, due to empty taxis and customers within the same neighborhood failing to pair efficiently. Spatial pricing can incentivize relocation of empty taxis to a neighborhood while the use of mobile applications can alleviate search friction within that neighborhood. We fit our model to a dataset of New York City taxi rides over four years and conduct a series of counterfactual studies. Our analysis reveals that improving search efficiency primarily impacts under-served neighborhoods such as upper Manhattan, Brooklyn, and Queens, while pricing primarily impacts well-served neighborhoods, for example, the airports, midtown, and downtown Manhattan. This underscores the value of a hybrid mechanism. In Chapter 2, we consider a ride-hailing platform that seeks to maximize its profit by dynamically dispatching cars to pick up customers and centrally relocating cars from one area to another. We model the ride-hailing platform as a closed stochastic processing network. Because the problem appears intractable, we resort to an approximate analysis in the heavy-traffic regime and consider the resulting Brownian control problem. This problem is simplified considerably and reduced to a lower-dimensional singular control problem called the workload formulation. We develop a novel algorithm to solve the workload problem numerically. We apply this algorithm to the workload problem derived from the New York City taxi dataset. The solution helps us derived a dynamic control policy for the New York City application. We demonstrate the effectiveness of the proposed dynamic control policy for the New York City example using a simulation study. In Chapter 3, we consider a make-to-stock manufacturing system selling multiple products to price-sensitive customers. The system manager seeks to maximize the long-run average profit by making dynamic pricing, outsourcing, and scheduling decisions: First, she adjusts prices dynamically depending on the system state. Second, when the backlog of work is judged excessive, she may outsource new orders thereby incurring outsourcing costs. Third, she decides dynamically on which product to prioritize in the manufacturing process, i.e., she makes dynamic scheduling decisions. This problem appears analytically intractable. Thus, we resort to an approximate analysis in the heavy-traffic regime and consider the resulting Brownian control problem. We solve this problem explicitly by exploiting the solution to a particular Riccati equation. The optimal solution to the Brownian control problem is a two-sided barrier policy with drift rate control: Outsourcing and idling processes are used to keep the workload process above the lower reflecting barrier and below the upper reflecting barrier, respectively. Between the two barriers, a state-dependent drift rate is used to control the workload process.

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