Decision, Operations & Information Technologies
Permanent URI for this communityhttp://hdl.handle.net/1903/2230
Prior to January 4, 2009, this unit was named Decision & Information Technologies.
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Item ECONOMETRIC ANALYSIS OF BIKE SHARING SYSTEMS(2022) Cao, Huan; Tunca, Tunay TT; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)I study the efficiency of the dockless bike sharing system, and how to utilize the operational decisions to improve system efficiency and profit. In the first chapter, I empirically analyze riders' economic incentives in a dockless bike sharing system and explore how to improve the efficiency of this business model. Specifically, I aim to answer three main questions: (i) What is the impact of the number of bicycles in the system on efficiency? (ii) How can bike relocation be best used to improve utilization? (iii) How do the efficiency of dockless and dock-based systems compare? To address these questions, I first build a microeconomic model of user decision-making in a dockless bike sharing system. I then use this model together with transaction-level data from a major dockless bike-sharing firm to structurally estimate the customer utility and demand parameters. Using this estimation in counterfactual analysis, we find that the company can decrease the bicycle fleet size by 40% while maintaining 90% of transactions, leading to estimated savings of $6.5 Million. We further find that a spatial bicycle rebalancing system based on our customer utility model can improve daily transactions by approximately 19%. Finally, we demonstrate that without bicycle redistribution, a smartly designed dock-based system can significantly outperform a dockless system. Our model provides a utility-based model that allows companies to estimate not only transactions, but also the time and location of lost potential demand, which can be used to make targeted improvements to the geographic bike distribution. It also allows managers to fine-tune bicycle fleet sizes and spatial rebalancing parameters. Further, our structural demand modeling can be used to improve the efficiency of dock-based systems by helping with targeted dock location decisions. In the second chapter, using data from a major dockless bike sharing system in Beijing, I study the subscription behavior and its relationship with the service level and price. Specifically, I develop an econometric model to study the subscription behavior for both existing subscribers and new sign-ups, respectively, and build up a functional relationship between the service level and system demand level and reveal the dynamics and interplays among subscription, system demand, and service level, which helps to recover the evolution of the number of subscribers over time. Based on all the estimation results and functional relationships, I then construct an empirical framework and straighten out the relationship between company profit and bicycle fleet size/subscription price. The counterfactual results show that the marginal benefit of deploying a larger bicycle fleet is decreasing, and the company should be cautious in determining and adjusting the bicycle fleet size. In addition, the examination demonstrates that the current price is too low, and raising the price properly can achieve about a 25% profit increase. The results also show the value of understanding riders' sensitivity to price and how to use it to better their operational decisions and accomplish better financial results. The counterfactual framework I proposed can be utilized in various policy evaluations and provides important insights and recommendations to the bikeshare companies and regulators.Item An Operations Management Framework to Improve Geographic Equity in Liver Transplantation(2022) Akshat, Shubham; Raghavan, S.; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the United States (U.S.), on average three people die every day awaiting a liver transplant for a total of 1,133 lives lost in 2021. While 13,439 patients were added to the waiting list in 2021, only 9,236 patients received liver transplantation. To make matters worse, there is significant geographic disparity across the U.S. in transplant candidate access to deceased donor organs. The U.S. Department of Health and Human Services (HHS) is keen to improve transplant policy to mitigate these disparities. The deceased donor liver allocation policy has been through three major implementations in the last nine years, but yet the issue persists. This dissertation seeks to apply operations management models to (i) understand transplant candidate behavior, and (ii) suggest improvements to transplant policy that mitigate geographic disparity. In the first essay, we focus on reducing disparities in the organ supply to candidate demand (s/d) ratios across transplant centers. We develop a nonlinear integer programming model that allocates organ supply to maximize the minimum s/d ratios across all transplant centers. We focus on circular donation regions that address legal issues raised with earlier organ distribution frameworks. This enables reformulating our model as a set-partitioning problem and our proposal can be viewed as a heterogeneous donor circle policy. Compared to the current Acuity Circles policy that has fixed radius circles around donation locations, the heterogeneous donor circle policy greatly improves both the worst s/d ratio, and the range of s/d ratios. With the fixed radius policy of 500 nautical miles (NM) the s/d ratio ranges from 0.37 to 0.84 at transplant centers, while with the heterogeneous circle policy capped at a maximum radius of 500NM the s/d ratio ranges from 0.55 to 0.60, closely matching the national s/d ratio of 0.5983. Broader sharing of organs is believed to mitigate geographic disparity. Recent policies are moving towards broader sharing in principle. In the second essay, we develop a patient's dynamic choice model to analyze her strategic response to a policy change. First, we study the impact of the Share 35 policy, a variant of broader sharing introduced in 2013, on the behavioral change of patients at the transplant centers (i.e., change in their organ acceptance probability), geographic equity, and efficiency (transplant quality, offer refusals, survival benefit from a transplant, and organ travel distance). We find that sicker patients became more selective in accepting organs (acceptance probability decreased) under the Share 35 policy. Second, we study the current Acuity Circles policy and conclude that it would result in lower efficiency (more offer refusals and a lower transplant benefit) than the previous Share 35 policy. Finally, we show that broader sharing in its current form may not be the best strategy to balance geographic equity and efficiency. The intuition is that by indiscriminately enlarging the pool of supply locations from where patients can receive offers, they tend to become more selective, resulting in more offer rejections and less efficiency. We illustrate that the heterogeneous donor circles policy that equalizes the s/d ratios across geographies is better than Acuity Circles in achieving geographic equity at the lowest trade-off on efficiency metrics. The previous two essays demonstrate the benefit of equalizing the s/d ratios across geographies. In December 2018 the Organ Procurement and Transplantation Network (OPTN) Board of Directors approved the continuous distribution framework as the desired policy goal for all the organ allocation systems. In this framework, the waiting list candidates will be prioritized based on several factors, each contributing some points towards the total score of a candidate. The factors in consideration are medical severity, expected post-transplant outcome, the efficient management of organ placement, and equity. However, the respective weights for each of these potential factors are not yet decided. In the third essay, we consider two factors, medical severity and the efficient management of organ placement (captured using the distance between the donor hospital and transplant center), and we design an allocation policy that maximizes the geographic equity. We develop a mathematical model to calculate the s/d ratio of deceased-donor organs at a transplant center in a continuous scoring framework of organ allocation policy. We then formulate a set-partitioning optimization problem and test our proposals using simulation. Our experiments suggest that reducing inherent differences in s/d ratios at the transplant centers result in saving lives and reduced geographic disparity.Item DATA-DRIVEN ESSAYS: ROLE OF PRICING AND RETURNS(2022) Hemmati, Sahar; Elmaghraby, Wedad J; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, we identify leavers in retailers’ operations such as their shipping policy design as well as markdown strategy which can significantly impact their product returns and the costs associated with it. We study how a high free shipping thresholds can induce shoppers to place orders with an intention to return a part of the order later. Using an empirical approach, which exploits natural experiments, we confirm that high threshold and/or high shipping fees induce a substantial order padding behavior, which leads to lower sales revenue, after adjusting for returns. However, we find that such behavior can be largely alleviated with frictions to returns. We propose that retailers looking to design their shipping policy should correctly account for return environment features. We also explore the link between a retailer’s markdown pricing strategies and its impact on customers’ purchase and return behavior. We specifically study the distinction between regular price markdowns and bundle price markdowns and the key contributors to such distinctions, and how they contribute to sales and customers’ return behavior. Capturing this notion and the heterogeneous impact of bundle and regular discounts on merchandise with different substitutability or complementarity as well as correlation between items at return stage, we offer recommendations on how a retailer should approach the design of their markdown strategies.Item Data Analytics and Mathematical Models to Facilitate Disease Prevention in the U.S.(2020) Apergi, Lida Anna; Baras, John; Golden, Bruce; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The U.S. is leading in healthcare expenditures worldwide, but health outcomes in the U.S. are not reflective of the level of spending. Prevention plays a crucial role in improving the health of individuals in the U.S., since it helps people live longer and healthier lives. Preventive services include actions that prevent diseases from ever occurring, detect diseases at an early stage, and manage diseases that have already been diagnosed. In this dissertation, we use data analytics and mathematical modeling techniques to better understand factors that influence disease prevention and help provide efficient solutions. In the first part of this dissertation, we study two problems of disease prevention at the public health level. First, we investigate the impact of state-level vaccination exemption policy and of the highly publicized Disneyland measles outbreak on MMR vaccination rates of young children. At the same time, we highlight the impact that the choice of socioeconomic factors can have on measurement results. We estimate the impact of these policies using multiple linear regression. Furthermore, we study the sensitivity of the results by examining a number of different approaches for the selection of socioeconomic control variables. Second, we utilize big data to estimate the additive cost of chronic diseases and study their cost patterns. We model the cost based on a cost hierarchy; that is, the cost of each condition is modeled as a function of the number of other more expensive chronic conditions the individual has. Using large scale claims data, we identify members that suffer from one or more chronic conditions and estimate their healthcare expenditures. Through our analysis, we categorize the chronic conditions into different expenditure groups based on the characteristics of their cost profiles. In the second part of this dissertation, we study two problems of disease prevention at the healthcare provider level, focusing in the area of cardiology. First, we study the adoption of conversational agent technology by patients with heart failure. Conversational agents can help patients with heart failure to manage their condition and provide frequent feedback to their healthcare providers. We analyze data from two studies, with each study focusing on a different type of conversational agent. We compare the two types of conversational agent technologies in terms of patient engagement, and investigate which patient characteristics are important in determining the patient engagement. Second, we tackle the problem of outpatient scheduling in the cardiology department of a large medical center. The outpatients have to go through a number of diagnostic tests and treatments before they can complete the final procedure. We develop an integer programming model to schedule appointments that are convenient for the outpatients by minimizing the number of visits that the patients have to make to the hospital and the time they spend waiting in the hospital. Furthermore, we investigate whether scheduling outpatients in groups can lead to better schedules for the patients.