@article{Manufacturing:2595,
      recid = {2595},
      author = {Barjesteh, Nasser},
      title = {Pricing and Matching in Service and Manufacturing Systems},
      publisher = {The University of Chicago},
      school = {Ph.D.},
      address = {2020-08},
      pages = {285},
      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.},
      url = {http://knowledge.uchicago.edu/record/2595},
      doi = {https://doi.org/10.6082/uchicago.2595},
}