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Open Access Publications from the University of California

Interdisciplinary Research in Operations Management: Applications in Healthcare, Retailing and On-demand Service Platforms

  • Author(s): Bai, Jiaru
  • Advisor(s): Keller, Robin L
  • Yin, Shuya
  • et al.
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Abstract

This dissertation consists of three essays on applications of interdisciplinary research in operations management. The first essay addresses issues in healthcare. Our goal is to evaluate the cost-effectiveness of bevacizumab compared to the baseline treatment with only chemotherapy in recurrent/persistent and metastatic cervical cancer using recently reported updated survival and toxicology data. We developed a Markov model with 5 patient health states for both treatments. With data based on the Gynecologic Oncology Group 240 randomized trials and the 2013 MediCare Services Drug Payment Table and Physician Fee Schedule, we present monthly transition probabilities and cost data. Our results show that chemotherapy plus bevacizumab can delay progression, but incur more complications.

The second essay lies at the interface between operations management and marketing. We aim to understand the tradeoffs in offering outlet stores. In particular, we study how much differentiation should be kept between the main and outlet stores from three perspectives: price, product and location. We find that an outlet store is more likely to be opened when travel sensitivity is lower or costs associated with it are lower. Moreover, offering an outlet store encourages the rm to improve the quality of the product sold in the main store as to reduce the cannibalization effect. We also observe that location differentiation has a substitution effect on quality and price differentiation.

In the third essay, we study several operational challenges for the on-demand service platforms. We consider a situation when an on-demand service platform uses earning sensitive independent providers with heterogeneous reservation prices to serve its time and price sensitive customers with heterogeneous valuation of the service. We present a queueing model with endogenous supply and endogenous demand to model this on-demand service platform. Based on our analysis, we find that it is optimal for the platform to charge a higher price, pay a higher wage, and offer a higher payout ratio when the potential customer demand increases. We use a set of actual data from a large on-demand ride-hailing platform in numerical experiments to illustrate some of our main insights.

Main Content

This item is under embargo until May 30, 2020.