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Author ORCID Identifier
Campus-Only Access for One (1) Year
Doctor of Philosophy (PhD)
Year Degree Awarded
Month Degree Awarded
Management Sciences and Quantitative Methods | Operations and Supply Chain Management | Technology and Innovation | Transportation and Mobility Management
The swift development and rollout of effective vaccines against the new coronavirus have reminded human beings of the power of emerging science and technology to change the world. Although the most widely used COVID-19 vaccines based on ``new" mRNA technology have been produced almost instantly, the roots of this technology date back to research conducted in the 1970s. As the old saying goes in the technology industry: It takes years to become an overnight success. So what other emerging technologies are about to burst into prominence, reshape the market, and change society? In this thesis, we identify several promising technologies that we believe will significantly impact industry and society in the near future, and address certain operational problems for these technologies. We study three novel practical problems related to managing emerging technologies, namely electric-powered vehicles and blockchain. The first two problems consider the management of Unmanned Aerial Vehicles (UAVs) by studying various economic and technological factors. The third problem focuses on the application of blockchain technology in Electric Vehicles (EVs) charging payment systems. Both UAVs and EVs fall under the category of electric-powered vehicles.
In the first problem, we consider both strategic and tactical decisions that e-commerce companies face in UAV-based delivery operations, and derive policies on when to offer UAV delivery, what delivery capacity to maintain, and what prices to charge for such deliveries. To this end, we develop a Markov Decision Process (MDP) framework, and introduce two heuristic procedures, through which near-optimal closed-form solutions can be obtained. The results are aimed at helping online retailers to determine in real time whether and to what extent to offer UAV-based delivery for given product categories in different service zones. In addition, we study delivery fee structures and identify UAV-based delivery pricing strategies under two widely used delivery pricing schemes. For capacity planning decisions, we describe an algorithm to identify the fleet size to utilize in order to fulfill uncertain demand in a given service region. We also identify structural characteristics on how these decisions and the expected profit are affected by changes in various problem parameters, which can generate generic insights on UAV-based delivery operations for e-commerce companies. We find that retailers should prioritize more profitable items when allocating UAV delivery capacity, and invest in adding more UAVs when per order opportunity costs are higher and promised delivery time thresholds are shorter. Retailers can potentially boost their net profits by increasing the effective promised delivery time threshold and/or decreasing the effective delivery delay costs and per order opportunity costs.
In the second problem, we focus on the UAV path planning problem, aiming to offer safe and cost-efficient flight paths for UAV missions under uncertain weather conditions. Specifically, we seek answers for the following research questions: 1. Given uncertain weather conditions and all relevant costs, what would be a safe and effective initial path for a UAV mission? 2. As weather conditions evolve over time, how can the UAV path be updated accordingly? 3. How does the optimal path change as the primary parameters in the system change? 4. How would optimal policies differ for different stakeholders in UAV operations? Building on a stochastic programming framework, we design a decision support system for UAV path planning under the consideration of stochastic weather evolution and related environmental, economic, and social costs. Our work contributes to this emerging research area by offering an optimal initial path for a given mission and insights about updating the path according to evolving weather conditions. Our proposed model is dynamic and data-driven and allows for safe and effective path planning. It is able to deal with real weather data from radars, and allows for safe and effective path planning while also minimizing any involved costs during each mission. Moreover, we conduct a detailed numerical analysis to demonstrate that the proposed system works well for multiple types of UAVs and missions while significantly reducing costs and social values.
In the third problem, we focus on the use of blockchain technology for EV operations. Our work contributes to this newly emerging area by expanding the practical application of blockchain technology, and by addressing the privacy and timing issues in payments for EV charging. Specifically, we study the optimal design of a blockchain-based EV charging payment system in a network of EVs and charging stations by investigating the following three practical research questions: 1. Which payment channels should be established between pairs of stations given the stochasticity of EV payment transactions? 2. What should be the capacity of these payment channels? In other words, how much a station should deposit into the on-chain escrow account? 3. How should the channel capacities be updated as demand realizes over time? To this end, we develop a two-stage stochastic programming model framework and introduce two different payment network designs: a centralized system and a collaborative system. By considering an off-chain implementation with fast transaction speeds, the proposed framework is capable of eliminating the high transaction fees and verification times, and establishing a privacy-aware payment system that companies can quickly deploy in real-time. A detailed numerical analysis is conducted to demonstrate that we can achieve optimal or near-optimal system costs in both systems through carefully modifying key practical parameters.
Overall, this study represents one of the early investigations into operations management in emerging technologies, namely UAVs, EVs, and blockchain technology. More specifically, this research involves deriving tactical and strategic policies for UAV-based delivery systems, developing a decision support system for UAV path planning, and investigating blockchain technology's application in EV charging payment systems. The results of this study are expected to offer managerial insights and improve efficiency and sustainability for different stakeholders and society in general.
Hu, Zhangchen, "Operations Management Applications in Emerging Technologies: The Cases of Electric-Powered Vehicles and Blockchain Technology" (2023). Doctoral Dissertations. 2898.
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