Multi-Period Stochastic Resource Planning: Models, Algorithms and Applications

This research addresses the problem of sequential decision making in the presence of uncertainty in the professional service industry. Specifically, it considers the problem of dynamically assigning resources to tasks in a stochastic environment with both the uncertainty of resource availability due to attrition, and the uncertainty of job availability due to unknown project bid outcome. This problem is motivated by the resource planning application at the Hewlett Packard (HP) Enterprises. The challenge is to provide resource planning support over a time horizon under the influence of internal resource attrition and demand uncertainty. To ensure demand is satisfied, the external contingent resources can be engaged to make up for internal resource attrition. The objective is to maximize profitability by identifying the optimal mix of internal and contingent resources and their assignments to project tasks under explicit uncertainty. While the sequential decision problems under uncertainty can often be modeled as a Markov decision process (MDP), the classical dynamic programming (DP) method using the Bellman’s equation suffers the well-known curses-of-dimensionality and only works for small size instances. To tackle the challenge of curses-of-dimensionality this research focuses on developing computationally tractable closed-loop Approximate Dynamic Programming (ADP) algorithms to obtain near-optimal solutions in reasonable computational time. Various approximation schemes are developed to approximate the cost-to-go function. A comprehensive computational experiment is conducted to investigate the performance and behavior of the ADP algorithm. The performance of ADP is also compared with that of a rolling horizon approach as a benchmark solution. Computational results show that the optimization model and algorithm developed in this thesis are able to offer solutions with higher profitability and utilization of internal resource for companies in the professional service industry

Data-Based Spatial and Temporal Modeling for Surface Variation Monitoring in Manufacturing.

Spatiotemporal processes exist widely in manufacturing, such as tool surface degradation in ultrasonic metal welding and surface shape progression in high-precision machining. High-resolution characterization and monitoring of spatiotemporal processes are crucial for manufacturing process control. The rapid development of 3D sensing technologies has made it possible to generate large volumes of spatiotemporal data for process characterization and monitoring. However, critical challenges exist in effectively acquiring and utilizing such spatiotemporal data in manufacturing, e.g., a high cost in the acquisition of high-resolution spatiotemporal data and a lack of systematic approaches for modeling multi-source data and monitoring spatiotemporal processes. To address these challenges, this dissertation carries out three research tasks for the development of collecting, modeling and monitoring spatiotemporal data. Specifically, (1) A novel dynamic sampling design algorithm is developed to efficiently characterize spatiotemporal processes in manufacturing. A state-space model and Kalman filter are used to predictively determine the measurement locations using a criterion considering both the prediction variance and the measurement costs. The determination of measurement locations is formulated as a binary integer programming problem, and genetic algorithm is applied for searching the optimal design. In addition, a new test statistic is proposed to monitor and update the temporal transition parameters in the spatiotemporal model. (2) A new surface modeling approach is devised to cost-effectively assess spatial surface variations by integrating an engineering model with multi-task Gaussian process (GP) learning. Surface variation is decomposed into a global trend which is induced by process variables and a zero-mean GP which shares commonality among multiple similar-but-not-identical processes. An iterative algorithm is developed to simultaneously estimate the process-specific parameters and the GP parameters. (3) A tool condition characterization and monitoring framework is developed for ultrasonic metal welding in lithium-ion battery manufacturing. The geometric progression of the tool surfaces is characterized using high-resolution spatiotemporal data. Classification algorithms are developed with monitoring features extracted from both the space and frequency domains. A novel impression measurement method is designed to effectively measure tool surfaces without the need of disassembling tools for off-line measurement.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120743/1/chshao_1.pd

Determinants of U.S. corporate credit spreads.

This thesis deals with various issues regarding determinants of US corporate credit spreads. These spreads are estimated as the difference between yields to maturity for corporate bonds and default-free instruments (Treasury bonds) of the same maturity. Corporate credit spreads are considered as measures of default risk. However, the premium required by investors for holding risky rather than risk-free bonds will incorporate a compensation not only for the default risk but also for other factors related to corporate bonds such as market liquidity or tax differential between corporate and Treasury bonds. In this study we firstly examine the relationship between bond ratings and credit spreads given that bond rating changes are expected to carry some informational value for debt investors. The findings indicate that bond ratings generally carry some informational value for corporate bond investors. The Granger causal relationship is more evident for negative watch lists and during periods of uncertainty in financial markets. In line with previous studies, our results suggest that changes in credit spreads are significantly related to interest rate levels, systematic risk factors (Fama and French) factors and equity returns

Econometrics with gretl. Proceedings of the gretl Conference 2009.

This book contains the articles presented at the first International gretl Conference, held on may 28-29, 2009 in Bilbao, Spain.Econometrics, gretl, open source, statistical software

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Statistical Foundations of Actuarial Learning and its Applications

This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice. Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features. Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus

Focusing on Updating Expectations and Perceptions in Platform Service Use

학위논문(박사) -- 서울대학교대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2022. 8. 이종수.The marketing literature clearly defines that consumers’ intention to repurchase a product or continue to use a service depends primarily on their prior experience of using them, and that continued user satisfaction is considered the key to building and retaining a loyal base of long-term customers. However, most existing studies use static utility models to explain consumer behavior in platform services and therefore do not adequately reflect the time-varying effects of continued use of the service. In addition, cross-sectional studies of consumers’ continued use of services cannot provide an accurate view of how customers’ expectations and perceptions of the product/service may change over time. Therefore, dynamic longitudinal studies are needed to determine how customers update their expectations and perceptions through experience and how this may affect customer satisfaction and/or behavior. This study aims to fill this gap by employing a dynamic utility model to explain consumer behavior in a platform economy where services are used repeatedly. Through an empirical study, we examine the time-varying effects of covariates in explaining consumers' use of ride-hailing platforms by first identifying the effect of updating expectations and perceptions with repeated use, thereby extending upon the expectation-confirmation theory. In the second part of this study, we observe the temporal effects on consumers' usage behavior through semiparametric modeling. The results of this study are expected to add to the literature on consumer behavior by presenting how the discrepancy between updated service expectations and actual service delivery, as well as updated perceptions, affect consumer behavior in platform services and by demonstrating seasonality in services with repeated use.서비스에 대한 고객 충성도를 야기하고 유지하기 위해서는 지속적으로 소비자를 만족시켜야 하고, 소비자의 서비스 재이용 여부는 해당 서비스와 관련하여 축적된 소비자의 이용 경험에 따라 달라진다는 것은 마케팅 문헌에서 익히 알려져 있다. 그러나 기존 연구는 재이용이 빈번한 플랫폼 서비스에서의 사용자 행태를 분석하는데 있어 정적 효용 모형을 사용하므로, 서비스의 지속 사용에 따른 시간 변동 효과를 적절하게 보이지 못하고 있다. 또한 소비자의 지속적인 사용에 따른 고객의 서비스에 대한 기대치 및 인식이 변화할 수 있음을 반영하지 못한다. 본 연구에서는 동적 효용 모형을 채택함으로써 플랫폼 사용자가 서비스 이용 경험에 기반하여 서비스에 대한 기대치 및 인식을 조정할 수 있음을 반영하고, 서비스에 대한 기대치와 실제 경험의 차이가 서비스의 만족도에 어떻게 영향을 미치는지를 알아보고자 한다. 또한 반모수 모델링을 통해 소비자의 서비스 이용 행태에서의 공변량의 시간적 특성을 알아본다. 분석 결과, 서비스에 대한 ‘서비스 격차’ 및 ‘인식 격차’는 서비스 만족도에 영향을 미치며, 그 영향 수준은 경험이 누적됨에 따라 변화함을 알 수 있었다. 또한, 누적된 경험에 기반하여 조정된 소비자의 서비스에 대한 인식과 실제 서비스 이용 경험 간의 차이가 서비스 만족에 대한 경험 누적 효과를 가장 잘 설명함을 알 수 있었다. 마지막으로 소비자의 서비스 이용에 있어 계절적 특성이 있음을 알 수 있었다. 이에 마케팅 관점에서 공변량에 대한 시간적 효과를 반영하지 못하면 소비자의 행동 변화를 잘못 감지할 가능성이 있으므로, 마케팅 전략 수립에 있어 서비스 재이용에 따른 특성 및 계절성을 적절히 반영할 필요가 있음을 알 수 있었다.Chapter 1. Introduction 12 1.1 Research Background 12 1.2 Research Objectives 15 Chapter 2. Literature Review 17 2.1 Studies on Consumer Behavior 17 2.1.1 Expectation-Confirmation Theory 17 2.1.2 Studies on Consumer's Continued Use of IT Service 20 2.1.3 The GAP Model of Service Quality 23 2.1.4 Studies on Seasonality of Consumer Behaviors 26 2.1.5 Studies on Online Platform Service Use 27 2.2 Models with Time Effect 33 2.2.1 Fitting Data with Spline 33 2.2.2 Varying Coefficient Models 35 2.2.3 Discrete Choice Models with Time Effect in Attributes 37 2.3 Deep Learning Models for Data Prediction 39 2.3.1 Recurrent Neural Network (RNN) 39 2.3.2 Long Short-Term Memory (LSTM) 42 2.3.3 Applications of Deep Learning in Consumer Studies 44 2.4 Limitations of Previous Literature and Research Motivation 45 Chapter 3. Methodology 47 3.1 Methodological Framework 47 3.2 Model Specification 48 3.2.1 Generic Model 48 3.2.2 Functions with Time-Varying Parameters 49 3.2.3 Smoothing Splines and Penalized Regression 51 3.2.4 Estimation Method 57 3.2.5 Parameter Selection 60 Chapter 4. Simulation Study 66 4.1 Validation of P-spine Implementation 66 4.2 Functional Case Studies 70 4.3 Comparison of Fit by Parameter Selection Method 77 Chapter 5. Empirical Study 83 5.1 Research Background 84 5.2 Data 90 5.3 Model Specification 92 5.3.1 Covariates of Time and Cost 92 5.3.2 The Interaction of Trip Distance and Travel Speed 93 5.3.3 Formation of Consumer Expectations 95 5.3.4 Estimation of Smoothing Coefficient for Error Adaption 99 5.4 Estimation Results 101 5.4.1 The Generic Model 101 5.4.2 Accumulated Experience Effect 121 5.4.3 Stream-of-Time Effects (by Times of the Day) 176 Chapter 6. Conclusion 217박