72 research outputs found
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Service Improvement and Cost Reduction for Airlines: Optimal Policies for Managing Arrival and Departure Operations under Uncertainty
No full textAnnual U.S. air travel demand has been growing steadily by 4-5% over the last decade, and it is estimated that the demand will nearly double in the next twenty years. It has also been estimated by the International Civil Aviation Organization that global demand for commercial aircraft will increase at an average annual rate of 4.1% by 2034 (IATA, 2014). However, airport expansions and aviation infrastructure upgrades have not kept pace with the increase in air traffic demand, as only 3% of all the new airport projects around the world are planned in the U.S. (CAPA, 2015). Thus, the operation rates at existing airports are likely to increase significantly, implying a greater need to increase the utilization of currently available runway capacity. With steadily increasing demand in air traffic and limited airport capacity, delay in air traffic is ubiquitous. Approximately 25% of flights experience delays of at least 15 minutes each year, resulting in significant passenger service issues and costs to airlines and society in general. Delays constitute the top service complaint for airlines, which has implications for the society as a whole - both economically and environmentally. Flight delays also increase airline costs directly, due to associated additional fuel, crew and maintenance costs. Recent studies show that the estimated cost of air transportation delay to the American economy ranges from 41 billion a year, of which, 29 million if such implementations are adapted by major airports in the U.S. Of these savings, 22 million per year if the proposed policies are implemented. I also find that the optimal metering configurations are mostly robust under different operating conditions. In addition, my results suggest that early spacing adjustments near the top of descent (TOD) are of more value for larger volumes of air traffic. In the third and fourth problems, I study optimal departure operations at airports under the context of departure metering, which is an airport surface management procedure that limits the number of aircraft on the runway by holding aircraft at a predesigned metering area. More specifically, in the third problem, I develop a stochastic dynamic programming framework for tactical management of pushback operations at gates and for determining the optimal number of aircraft to be directed to the runway from the metering areas. I introduce four easy-to-implement practical departure metering policies and implement a comparative analysis between these practical policies and the optimal numerical solutions. I also implement sensitivity analysis of the departure metering policies over state variable values. In the fourth problem, I study the optimal metering area capacity at the strategic level. Building on the dynamic programming framework mentioned in the third problem, I identify the optimal metering area capacity using marginal analysis to minimize expected overall costs. Numerical simulations are implemented and potential savings are identified for sample U.S. airports based on varying capacity levels. The optimal metering area capacity is then determined based on the numerical implementations to further improve overall efficiency and sustainability of departure operations. I also analyze the benefits to airlines in terms of annual savings due to such policies, and find that the annual savings could be $31 million if the optimal departure metering policies are implemented at the top ten major airports in the U.S. Overall, as one of the few studies on stochasticity in arrival and departure operations, I derive both tactical and strategic policies to improve efficiency and sustainability for airlines and the society, which can enhance service quality and strengthen market position for the airlines involved.ManagementDoctor of Philosophy (Ph.D.
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Operations Management Applications in Emerging Technologies: The Cases of Electric-Powered Vehicles and Blockchain Technology
No full textThe 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.Doctor of Philosophy (Ph.D.
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Managing Information Security Investments Under Uncertainty: Optimal Policies for Technology Investment and Information Sharing
No full textInformation systems are an integral part of today's business environment. Businesses, government organizations, and the society rely on these systems for various transactions, most of which have huge financial implications. Hence, attacks that breach information systems result in interruption of operations, loss of data and customer confidence, constituting a significant threat to firms. The losses due to attacks on information systems can be mitigated through investments in information security technologies and services. In this thesis we study three practical problems related to information system security investment management: (1) Optimal policies for technology investment in information system security; (2) Optimal policies for information sharing in information system security; and (3) Asymmetric information sharing in information system security. We believe that firms can benefit from this work either through direct implementation for specific guidance, or through indirect use of several policy results obtained. An important characteristic of this studies is that we build this models by using real-world data through survey to information system security practitioners. As one of the few studies on information system security investment management through operations management approaches, this work also set the first step for futures studies on related topics that can be explored by researchers in the field of management science.ManagementDoctor of Philosophy (Ph.D.
Advanced Multi-Stage Local Search Applications to Vehicle Routing Problem with Time Windows: A Review
No full text... This paper presents a survey of the latest research motivated by this recognition. The presentation is focused on multi-stage applications of advanced local search techniques on the VRPTW. Multi-stage algorithms optimize the number of vehicles and travel time independently in order to ensure that the search is directed towards the achievement of the primary objective. Basic features of these algorithms, as well as hybridization strategies are described. For most algorithms, experimental results on Solomon's benchmark test problems are provided and analyze
Efficient Solution Procedures for Multistage Stochastic Formulations of Two Problem Classes
No full textWe consider two classes of stochastic programming models which are motivated by two applications related to the field of aviation. The first problem we consider is the network capacity planning problem, which arises in capacity planning of systems with network structures, such as transportation terminals, roadways and telecommunication networks. We study this problem in the context of airport terminal capacity planning. In this problem, the objective is to determine the optimal design and expansion capacities for different areas of the terminal in the presence of uncertainty in future demand levels and expansion costs, such that overall passenger delay is minimized. We model this problem as a nonlinear multistage stochastic integer program with a multicommodity network flow structure. The formulation requires the use of time functions for maximum delays in passageways and processing stations, for which we derive approximations that account for the transient behavior of flow. The deterministic equivalent of the developed model is solved via a branch and bound procedure, in which a bounding heuristic is used at the nodes of the branch and bound tree to obtain integer solutions. In the second study, we consider the project portfolio optimization problem. This problem falls in the class of stochastic programs in which times of uncertainty realizations are dependent on the decisions made. The project portfolio optimization problem deals with the selection of research and development (R&D) projects and determination of optimal resource allocations for the current planning period such that the expected total discounted return or a function of this expectation for all projects over an infinite time horizon is maximized, given the uncertainties and resource limitations over a planning horizon. Accounting for endogeneity in some parameters, we propose efficient modeling and solution approaches for the resulting multistage stochastic integer programming model. We first develop a formulation that is amenable to scenario decomposition, and is applicable to the general class of stochastic problems with endogenous uncertainty. We then demonstrate the use of the sample average approximation method in solving large scale problems of this class, where the sample problems are solved through Lagrangian relaxation and lower bounding heuristics.Ph.D
Optimal Metering Point Configurations for Optimized Profile Descent Based Arrival Operations at Airports
No full text Optimized profile descent (OPD) is an arrival procedure for the Next Generation Air Transportation System, which has been demonstrated to effectively decrease noise, emissions, and fuel costs. Implementation of OPD operations requires effective metering policies because of the increased role of uncertainty in aircraft trajectories during descent. While optimal sequencing and spacing of OPD flights have been studied in the literature, any potential savings resulting from possible changes in metering point configurations have not been addressed. In this paper, we develop models to further increase the value of OPD operations over conventional arrival procedures by optimizing metering point configurations, which include identification of the optimal number and locations of metering points to use during OPD. We derive an algorithmic framework based on implementations of a stochastic dynamic programming model and a nonlinear stochastic integer program to identify best metering point configurations where resulting computational difficulties are addressed through convex approximation and Lagrangian decomposition procedures. We also describe numerical results based on actual traffic information at major U.S. airports, which indicate that the total potential savings in the top 10 major airports could be up to $22 million a year if the proposed policies are implemented. The online appendix is available at https://doi.org/10.1287/trsc.2017.0788 . </jats:p
Climate change and optimal energy technology R&D policy
No full textPublic policy response to global climate change presents a classic problem of decision making under uncertainty. Theoretical work has shown that explicitly accounting for uncertainty and learning in climate change can have a large impact on optimal policy, especially technology policy. However, theory also shows that the specific impacts of uncertainty are ambiguous. In this paper, we provide a framework that combines economics and decision analysis to implement probabilistic data on energy technology research and development (R&D) policy in response to global climate change. We find that, given a budget constraint, the composition of the optimal R&D portfolio is highly diversified and robust to risk in climate damages. The overall optimal investment into technical change, however, does depend (in a non-monotonic way) on the risk in climate damages. Finally, we show that in order to properly value R&D, abatement must be included as a recourse decision.R&D portfolio Energy technology Climate change Stochastic programming Public policy
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