Open Access. Powered by Scholars. Published by Universities.^{®}
Operations Research, Systems Engineering and Industrial Engineering Commons^{™}
Open Access. Powered by Scholars. Published by Universities.^{®}
 Discipline
 Keyword

 Optimization (7)
 Stochastic Optimization (3)
 Interior point methods (2)
 Nonlinear Optimization (2)
 Reinforcement Learning (2)

 Nonconvex optimization (2)
 Smart grid (2)
 Simulation (2)
 Robust Optimization (2)
 Stochastic optimization (2)
 Quadratic Optimization (2)
 Machine learning (2)
 Deep Learning (2)
 Machine Learning (2)
 Acceleration (1)
 Army (1)
 Agile (1)
 Algorithmic Game Theory (1)
 Artificial intelligence (1)
 Basel II (1)
 Activeset (1)
 Additional information acquisition (1)
 Bilevel optimization (1)
 Bilevel Programming (1)
 Answer depth (1)
 Backtesting (1)
 Algorithms (1)
 BarzilaiBorwein and limited memory methods (1)
 Assign (1)
 Beer Game Problem (1)
 Publication Year
Articles 1  30 of 547
FullText Articles in Operations Research, Systems Engineering and Industrial Engineering
Distributed Algorithms In LargeScaled Empirical Risk Minimization: NonConvexity, AdaptiveSampling, And MatrixFree SecondOrder Methods, Xi He
Theses and Dissertations
The rising amount of data has changed the classical approaches in statistical modeling significantly. Special methods are designed for inferring meaningful relationships and hidden patterns from these large datasets, which build the foundation of a study called Machine Learning (ML). Such ML techniques have already applied widely in various areas and achieved compelling success. In the meantime, the huge amount of data also requires a deep revolution of current techniques, like the availability of advanced data storage, new efficient largescale algorithms, and their distributed/parallelized implementation.There is a broad class of ML methods can be interpreted as Empirical Risk ...
Conic Optimization: Optimal Partition, Parametric, And Stability Analysis, Ali MohammadNezhad
Conic Optimization: Optimal Partition, Parametric, And Stability Analysis, Ali MohammadNezhad
Theses and Dissertations
A linear conic optimization problem consists of the minimization of a linear objective function over the intersection of an affine space and a closed convex cone. In recent years, linear conic optimization has received significant attention, partly due to the fact that we can take advantage of linear conic optimization to reformulate and approximate intractable optimization problems. Steady advances in computational optimization have enabled us to approximately solve a wide variety of linear conic optimization problems in polynomial time. Nevertheless, preprocessing methods, rounding procedures and sensitivity analysis tools are still the missing parts of conic optimization solvers. Given the output ...
Solution Techniques For NonConvex Optimization Problems, Wei Xia
Solution Techniques For NonConvex Optimization Problems, Wei Xia
Theses and Dissertations
This thesis focuses on solution techniques for nonconvex optimization problems. The first part of the dissertation presents a generalization of the completely positive reformulation of quadratically constrained quadratic programs (QCQPs) to polynomial optimization problems. We show that by explicitly handling the linear constraints in the formulation of the POP, one obtains a refinement of the condition introduced in Bai's (2015) Thoerem on QCQPs, where the refined theorem only requires nonnegativity of polynomial constraints over the feasible set of the linear constraints. The second part of the thesis is concerned with globally solving nonconvex quadratic programs (QPs) using integer programming ...
Applications Of Machine Learning In Supply Chains, Afshin Oroojlooy
Applications Of Machine Learning In Supply Chains, Afshin Oroojlooy
Theses and Dissertations
Advances in new technologies have resulted in increasing the speed of data generation and accessing larger data storage. The availability of huge datasets and massive computational power have resulted in the emergence of new algorithms in artificial intelligence and specifically machine learning, with significant research done in fields like computer vision. Although the same amount of data exists in most components of supply chains, there is not much research to utilize the power of raw data to improve efficiency in supply chains.In this dissertation our objective is to propose datadriven nonparametric machine learning algorithms to solve different supply chain ...
Distributed Methods For Composite Optimization: Communication Efficiency, LoadBalancing And Local Solvers, Chenxin Ma
Distributed Methods For Composite Optimization: Communication Efficiency, LoadBalancing And Local Solvers, Chenxin Ma
Theses and Dissertations
The scale of modern datasets necessitates the development of efficient distributed opti mization methods for composite problems, which have numerous applications in the field of machine learning. A critical challenge in realizing this promise of scalability is to develop efficient methods for communicating and coordinating information between distributed ma chines, taking into account the specific needs of machine learning algorithms. Recent work in this area has been limited by focusing heavily on developing highly specific methods for the distributed environment. These specialpurpose methods are often unable to fully leverage the competitive performance of their welltuned and customized single machine counterparts ...
A Service System With OnDemand Agents, Stochastic Gradient Algorithms And The Sarah Algorithm, Lam Nguyen
A Service System With OnDemand Agents, Stochastic Gradient Algorithms And The Sarah Algorithm, Lam Nguyen
Theses and Dissertations
We consider a system, where a random flow of customers is served by agents invited ondemand. Each invited agent arrives into the system after a random time, and leaves it with some probability after each service completion. Customers and/or agents may be impatient. The objective is to design a realtime adaptive invitation scheme that minimizes customer and agent waiting times.We study some aspects of the SGD method with a fixed, large learning rate and propose a novel assumption of the objective function, under which this method has improved convergence rates. We also propose a convergence analysis of SGD ...
Quadratic Optimization For Nonsmooth Optimization Algorithms: Theory And Numerical Experiments, Baoyu Zhou
Quadratic Optimization For Nonsmooth Optimization Algorithms: Theory And Numerical Experiments, Baoyu Zhou
Theses and Dissertations
Nonsmooth optimization arises in many scientific and engineering applications, such as optimal control, neural network training, and others. Gradient sampling and bundle methods are two ef ficient types of algorithms for solving nonsmooth optimization problems. Quadratic optimization (commonly referred to as QP) problems arise as subproblems in both types of algorithms. This thesis introduces an algorithm for solving the types of QP problems that arise in such methods. The proposed algorithm is inspired by one proposed in a paper written by Krzysztof C. Kiwiel in the 1980s. Improvements are proposed so that the algorithm may solve problems with addi tional ...
Detection And Modeling Of Wind Ramp Events In Smart Grid, Xingbang Du
Detection And Modeling Of Wind Ramp Events In Smart Grid, Xingbang Du
Theses and Dissertations
Wind ramp events have a significant influence of uncertainty in wind power production. In order to build an efficient decisionmaking systems for the smart grid, developing statistical models based on analysis of historical data of wind ramp events is indispensable. In this paper, we design a detection algorithm to analyze historical data, build distribution models to predict and simulate wind ramp events. Phasetype distribution consists of a convolution of the Exponential distribution which can be used to apply Markov decisions process and identify the factors which can cause wind ramp events. We use three types of Phasetype distribution to fit ...
Calculating Vacancy Positions To Optiamally Assign Recruits Of The Rok Army, Doheon Han
Calculating Vacancy Positions To Optiamally Assign Recruits Of The Rok Army, Doheon Han
Theses and Dissertations
Since the number of troops in the ROK Army will gradually decrease, efficient personnel assignment is required to improve the level of combat power. Therefore, the process of assigning recruits will become more important. In the current system, the calculation of the vacancy positions for assigning recruits is performed manually by a person. Thus, the occurrence of mistakes in the process and the inefficiencies of the calculation results are inevitable problems. In particular, imbalances due to deviations among combat powers after the assignment of recruits can be a major problem. The purpose of the new model presented in this paper ...
RealTime Modeling Of GasElectric Dependencies: An Optimal Control Approach, Qinxu Gu
RealTime Modeling Of GasElectric Dependencies: An Optimal Control Approach, Qinxu Gu
Theses and Dissertations
In this thesis, several turbinegovernor models such as GAST model and GGOV1 model are implemented. The simulation of the models takes electric power and speed as the variable input, respectively, to see what will influence the fuel consumption and mechanical power. These two models are implemented in detail and incorporated with nonwindup limiters. Different implementations for the nonwindup limiters are considered and compared. In addition to the single gas turbine model, its integration to a power system model is developed as well. We consider twoarea systems, one with simple primary speed droop control and one with supplementary automatic generation control.
Estimation Of Hidden Markov Model, Xuecheng Yin
Estimation Of Hidden Markov Model, Xuecheng Yin
Theses and Dissertations
With the development of economy, estimation has gradually received attention. Economic performance is essential to a company, that's why data analyst is very popular. Since Dongfeng Motor Corporation is one of the magnate company in Chinese vehicle market, estimation the data of Dongfeng could be very meaningful. There are many methods used to estimate economic performance, in this thesis we mainly focus on Hidden Markov Model (HMM).First of all, the thesis introduces the basic concept of Markov Process and Hidden Markov model, including three classes of problems, evaluation, decoding, learning problems. Also, the thesis introduces the corresponding solution ...
The Inmate Transportation Problem, Anshul Sharma
The Inmate Transportation Problem, Anshul Sharma
Theses and Dissertations
The Inmate Transportation Problem (ITP) is a common complex problem in any correctional system. In this project we studied the present policies and practices used by the Pennsylvania Department of Corrections (PADoC) to transport inmates between 25 different state Correctional Institutions (CIs) across the state of Pennsylvania. As opposed to the current practice of manually deciding about transportation we propose a mathematical optimization approach.We develop a weighted multiobjective mixed integer linear optimization (MILO) model. The MILO model optimizes the transportation of the inmates within a correctional system. Particularly, the MILO model assigns inmates, who needs to be transported from ...
Exploiting Structures In MixedInteger SecondOrder Cone Optimization Problems For BranchAndConicCut Algorithms, Sertalp Bilal Cay
Exploiting Structures In MixedInteger SecondOrder Cone Optimization Problems For BranchAndConicCut Algorithms, Sertalp Bilal Cay
Theses and Dissertations
This thesis studies computational approaches for mixedinteger secondorder cone optimization (MISOCO) problems. MISOCO models appear in many realworld applications, so MISOCO has gained significant interest in recent years. However, despite recent advancements, there is a gap between the theoretical developments and computational practice. Three chapters of this thesis address three areas of computational methodology for an efficient branchandconiccut (BCC) algorithm to solve MISOCO problems faster in practice. These chapters include a detailed discussion on practical work on adding cuts in a BCC algorithm, novel methodologies for warmstarting secondorder cone optimization (SOCO) subproblems, and heuristics for MISOCO problems.The first part ...
Efficient Trust Region Methods For Nonconvex Optimization, Mohammadreza Samadi
Efficient Trust Region Methods For Nonconvex Optimization, Mohammadreza Samadi
Theses and Dissertations
For decades, a great deal of nonlinear optimization research has focused on modeling and solving convex problems. This has been due to the fact that convex objects typically represent satisfactory estimates of realworld phenomenon, and since convex objects have very nice mathematical properties that makes analyses of them relatively straightforward. However, this focus has been changing. In various important applications, such as largescale data fitting and learning problems, researchers are starting to turn away from simple, convex models toward more challenging nonconvex models that better represent realworld behaviors and can offer more useful solutions.To contribute to this new focus ...
Analysis Of Power Network Defense Under Intentional Attacks, Weiming Lei
Analysis Of Power Network Defense Under Intentional Attacks, Weiming Lei
Theses and Dissertations
In this thesis, we introduce a method to identify the most critical components (e.g., generators, transformers, transmission lines) in an existing electric power grid, that contains renewable (wind) generators. We assume the power system is under threat of intentional attacks. By learning the potentially best attacking plan, the system operator can have a better understanding of the most important components in the system. We use a bilevel optimization model to describe the problem and a decomposition approach to solve the bilevel model by finding maximally disruptive attack plans for attackers who have limited attacking resources. The testing data are ...
Stochastic Optimal Control Of GridLevel Storage, Yuhai Hu
Stochastic Optimal Control Of GridLevel Storage, Yuhai Hu
Theses and Dissertations
The primary focus of this dissertation is the design, analysis and implementation of stochastic optimal control of gridlevel storage. It provides stochastic, quantitative models to aid decisionmakers with rigorous, analytical tools that capture high uncertainty of storage control problems. The first part of the dissertation presents a $p$periodic Markov Decision Process (MDP) model, which is suitable for mitigating endofhorizon effects. This is an extension of basic MDP, where the process follows the same pattern every $p$ time periods. We establish improved nearoptimality bounds for a class of greedy policies, and derive a corresponding valueiteration algorithm suitable for periodic problems ...
Optimization Algorithms For Machine Learning Designed For Parallel And Distributed Environments, Seyedalireza Yektamaram
Optimization Algorithms For Machine Learning Designed For Parallel And Distributed Environments, Seyedalireza Yektamaram
Theses and Dissertations
This thesis proposes several optimization methods that utilize parallel algorithms for largescale machine learning problems. The overall theme is networkbased machine learning algorithms; in particular, we consider two machine learning models: graphical models and neural networks. Graphical models are methods categorized under unsupervised machine learning, aiming at recovering conditional dependencies among random variables from observed samples of a multivariable distribution. Neural networks, on the other hand, are methods that learn an implicit approximation to underlying true nonlinear functions based on sample data and utilize that information to generalize to validation data. The goal of finding the best methods relies on ...
Computational Methods For Discrete Conic Optimization Problems, Aykut Bulut
Computational Methods For Discrete Conic Optimization Problems, Aykut Bulut
Theses and Dissertations
This thesis addresses computational aspects of discrete conic optimization. Westudy two wellknown classes of optimization problems closely related to mixedinteger linear optimization problems. The case of mixed integer secondordercone optimization problems (MISOCP) is a generalization in which therequirement that solutions be in the nonnegative orthant is replaced by arequirement that they be in a secondorder cone. Inverse MILP, on the otherhand, is the problem of determining the objective function that makes a givensolution to a given MILP optimal.Although these classes seem unrelated on the surface, the proposedsolution methodology for both classes involves outer approximation of a conicfeasible region by ...
Optimization Of Surgery Scheduling In Multiple Operating Rooms With Post Anesthesia Care Unit Capacity Constraints, Miao Bai
Theses and Dissertations
Surgery schedules are subject to disruptions due to duration uncertainty in surgical activities, patient punctuality, surgery cancellation and surgical emergencies. Unavailable recovery resources, such as postanesthesia care unit (PACU) beds may also cause deviations from the surgical schedule. Such disruptions may result in inefficient utilization of medical resources, suboptimal patient care and patient and staff dissatisfaction. To alleviate these adverse effects, we study three open challenges in the field of surgery scheduling. The case we study is in a surgical suite with multiple operating rooms (ORs) and a shared PACU. The overall objective is to minimize the expected cost incurred ...
Essays On Risk Management In Portfolio Optimization And Gas Supply Networks, Onur Babat
Essays On Risk Management In Portfolio Optimization And Gas Supply Networks, Onur Babat
Theses and Dissertations
This work focuses on developing algorithms and methodologies to solve problems dealing with uncertainty in portfolio optimization and industrial gas networks. First, we study the MeanSemiVariance Project (MSVP) portfolio selection problem, where the objective is to obtain the optimal riskreward portfolio of nondivisible projects when the risk is measured by the semivariance of the portfolio's NetPresent Value (NPV) and the reward is measured by the portfolio's expected NPV. Similar to the wellknown MeanVariance portfolio selection problem, when integer variables are present (e.g., due to transaction costs, cardinality constraints, or asset illiquidity), the MSVP problem can be solved ...
Aspect Identification And Sentiment Analysis In TextBased Reviews, Sean Byrne
Aspect Identification And Sentiment Analysis In TextBased Reviews, Sean Byrne
Theses and Dissertations
Online textbased reviews are often associated with only an aggregate numeric rating that does not account for nuances in the sentiment towards specific aspects of the review's subject. This thesis explores the problem of determining review scores for specific aspects of a review's subject. Specifically, we examine two important subtasks  aspect identification (identifying specific words and phrases that refer to aspects of the review subject) and aspectbased sentiment analysis (determining the sentiment of each aspect). We examine two different models, conditional random fields and an association mining algorithm, for performing aspect identification. We also develop a method for ...
Duality And A Closer Look At Implementation Of Linear Optimization Algorithms, Naga Venkata Chaitanya Gudapati
Duality And A Closer Look At Implementation Of Linear Optimization Algorithms, Naga Venkata Chaitanya Gudapati
Theses and Dissertations
Duality played, and continues to play a crucial role in the advancement of solving LinearOptimization (LO) problems. In this thesis, we rst review the history of LO and varioussoftware to solve LO problems. In the next chapter, we discuss Pivot Algorithms, basistableaus, primal and dual Simplex methods and their computational implementation.Then we discuss Interior Point Methods (IPM) and the numerical linear algebra involvedin their implementation. The next chapter discusses duality in signicant detail, andthe role of duality in LO software design. We also describe the dualizing scheme usedto dualize the NETLIB test problems. We then discuss the computational results ...
Limited Memory Steepest Descent Methods For Nonlinear Optimization, Wei Guo
Limited Memory Steepest Descent Methods For Nonlinear Optimization, Wei Guo
Theses and Dissertations
This dissertation concerns the development of limited memory steepest descent (LMSD) methods for solving unconstrained nonlinear optimization problems. In particular, we focus on the class of LMSD methods recently proposed by Fletcher, which he has shown to be competitive with wellknown quasiNewton methods such as LBFGS. However, in the design of such methods, much work remains to be done. First of all, Fletcher only showed a convergence result for LMSD methods when minimizing strongly convex quadratics, but no convergence rate result. In addition, his method mainly focused on minimizing strongly convex quadratics and general convex objectives, while when it comes ...
Algorithmic Methods For Concave Optimization Problems With Binary Variables, Tao Li
Algorithmic Methods For Concave Optimization Problems With Binary Variables, Tao Li
Theses and Dissertations
In this thesis, we discuss two problems that involve concave minimization problems with binary variables.For the first problem, we compare the Lagrangian relaxation method and our reformulation method in solving the location model with risk pooling (LMRP) with constant customer demand rate and equal standard deviation of daily demand.Our new method is to reformulate the original nonlinear model for the LMRP as a linear one. In the reformulation, we introduce a set of parameters representing the increase in the cost of the nonlinear part for each additional cost assigned to a potential facility site, and a set of ...
Random Models In Nonlinear Optimization, Matthew Joseph Menickelly
Random Models In Nonlinear Optimization, Matthew Joseph Menickelly
Theses and Dissertations
In recent years, there has been a tremendous increase in the interest of applying techniques of deterministic optimization to stochastic settings, largely motivated by problems that come from machine learning domains. A natural question that arises in light of this interest is the extent to which iterative algorithms designed for deterministic (nonlinear, possibly nonconvex) optimization must be modified in order to properly make use of inherently random information about a problem.This thesis is concerned with exactly this question, and adapts the modelbased trustregion framework of derivativefree optimization (DFO) for use in situations where objective function values or the set ...
Conic Programming Approaches For Polynomial Optimization: Theory And Applications, Xiaolong Kuang
Conic Programming Approaches For Polynomial Optimization: Theory And Applications, Xiaolong Kuang
Theses and Dissertations
Historically, polynomials are among the most popular class of functions used for empirical modeling in science and engineering. Polynomials are easy to evaluate, appear naturally in many physical (realworld) systems, and can be used to accurately approximate any smooth function. It is not surprising then, that the task of solving polynomial optimization problems; that is, problems where both the objective function and constraints are multivariate polynomials, is ubiquitous and of enormous interest in these fields. Clearly, polynomial op timization problems encompass a very general class of nonconvex optimization problems, including key combinatorial optimization problems.The focus of the first three ...
Measures Of Information For Information Acquisition Optimization, Zhenglin Wei
Measures Of Information For Information Acquisition Optimization, Zhenglin Wei
Theses and Dissertations
The objective of this study is to find suitable methods of information measurement and characterizations to facilitate research on information acquisition optimization. Specifically, this study is to support an approach, which has been acquired in past periods of the research, that can be interpreted as a theory of information exchange between the decision maker and information source(s). It can also be said that the developed approach complements the classical Information Theory. The classical Information Theory describes the transmission of information over some channel, regardless of its content. The proposed approach deals the first and last link of the full ...
Model Fidelity And Its Impact On Power Grid Resource Planning Under High Renewable Penetration, Daniel Xavier Wolbert
Model Fidelity And Its Impact On Power Grid Resource Planning Under High Renewable Penetration, Daniel Xavier Wolbert
Theses and Dissertations
Renewable power generation resources are one of the biggest trends emerging thepower systems world. The inherent variability of these power sources brings challenges in terms of planning, reliability and feasibility factors. Classical formulationsof Optimal Power Flow systems tries to generate power for a system at minimalcost, considering devices and transmission constraints.This work evaluates the impact of different levels of renewable ”green” energy interms of the two most common optimization models in power systems planning: UnitCommitment and Economic Dispatch, under different time modelling and transmission assumptions.In this thesis a full userfriendly tool in AIMMS was built with4 datasets based ...
A DeaBased Approach To Evaluate The Efficiency Of NonHomogeneous Service Locations, Amer Husain Asiri
A DeaBased Approach To Evaluate The Efficiency Of NonHomogeneous Service Locations, Amer Husain Asiri
Theses and Dissertations
This study aims at evaluating the performance of a company, ‘XYZ Company’, that has 115 service locations. Because of its ability of handling large numbers of inputs and outputs, and removing the need of predefining the factors’ weights, Data Envelopment Analysis (DEA) is used. DEA is benchmark tool that measures the efficiency of entities with respect to each other by assessing their performance of utilizing inputs to produce outputs. Researchers have developed several DEA models, all of which have different characteristics.A main assumption of DEA is that the entities are homogeneous – i.e. operating under similar conditions, which is ...
Automated Car Guiding System Using Reinforcement Learning, Haoran Zhang
Automated Car Guiding System Using Reinforcement Learning, Haoran Zhang
Theses and Dissertations
The major objective of this project is to design and implement a car guiding system in a desksize area, with a remotecontrolled toy car. The software, including the calculation, image processing, and movement control, was coded with Python and C++. Qlearning algorithm was selected to be the core of the calculation part, and OpenCV library was used for image processing.The hardware consists of a webcam, a laptop, and a toy car with Bluetooth connection. Some wood boards were also used to build a frame for restraining the area for running the car.The system is able to track the ...