Open Access. Powered by Scholars. Published by Universities.®

Operations Research, Systems Engineering and Industrial Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

Articles 1 - 30 of 547

Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Distributed Algorithms In Large-Scaled Empirical Risk Minimization: Non-Convexity, Adaptive-Sampling, And Matrix-Free Second-Order Methods, Xi He Jan 2019

Distributed Algorithms In Large-Scaled Empirical Risk Minimization: Non-Convexity, Adaptive-Sampling, And Matrix-Free Second-Order 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 large-scale 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 Mohammad-Nezhad Jan 2019

Conic Optimization: Optimal Partition, Parametric, And Stability Analysis, Ali Mohammad-Nezhad

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 Non-Convex Optimization Problems, Wei Xia Jan 2019

Solution Techniques For Non-Convex Optimization Problems, Wei Xia

Theses and Dissertations

This thesis focuses on solution techniques for non-convex 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 non-convex quadratic programs (QPs) using integer programming ...


Applications Of Machine Learning In Supply Chains, Afshin Oroojlooy Jan 2019

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 data-driven non-parametric machine learning algorithms to solve different supply chain ...


Distributed Methods For Composite Optimization: Communication Efficiency, Load-Balancing And Local Solvers, Chenxin Ma Sep 2018

Distributed Methods For Composite Optimization: Communication Efficiency, Load-Balancing 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 special-purpose methods are often unable to fully leverage the competitive performance of their well-tuned and customized single machine counterparts ...


A Service System With On-Demand Agents, Stochastic Gradient Algorithms And The Sarah Algorithm, Lam Nguyen Aug 2018

A Service System With On-Demand 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 on-demand. 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 real-time 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 May 2018

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 May 2018

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 decision-making 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. Phase-type 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 Phase-type distribution to fit ...


Calculating Vacancy Positions To Optiamally Assign Recruits Of The Rok Army, Doheon Han May 2018

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 ...


Real-Time Modeling Of Gas-Electric Dependencies: An Optimal Control Approach, Qinxu Gu May 2018

Real-Time Modeling Of Gas-Electric Dependencies: An Optimal Control Approach, Qinxu Gu

Theses and Dissertations

In this thesis, several turbine-governor 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 non-windup limiters. Different implementations for the non-windup 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 two-area systems, one with simple primary speed droop control and one with supplementary automatic generation control.


Estimation Of Hidden Markov Model, Xuecheng Yin Mar 2018

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 Jan 2018

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 multi-objective 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 Mixed-Integer Second-Order Cone Optimization Problems For Branch-And-Conic-Cut Algorithms, Sertalp Bilal Cay Jan 2018

Exploiting Structures In Mixed-Integer Second-Order Cone Optimization Problems For Branch-And-Conic-Cut Algorithms, Sertalp Bilal Cay

Theses and Dissertations

This thesis studies computational approaches for mixed-integer second-order cone optimization (MISOCO) problems. MISOCO models appear in many real-world 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 branch-and-conic-cut (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 warm-starting second-order cone optimization (SOCO) subproblems, and heuristics for MISOCO problems.The first part ...


Efficient Trust Region Methods For Nonconvex Optimization, Mohammadreza Samadi Jan 2018

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 real-world 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 large-scale data fitting and learning problems, researchers are starting to turn away from simple, convex models toward more challenging nonconvex models that better represent real-world behaviors and can offer more useful solutions.To contribute to this new focus ...


Analysis Of Power Network Defense Under Intentional Attacks, Weiming Lei Jan 2018

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 Grid-Level Storage, Yuhai Hu Jan 2018

Stochastic Optimal Control Of Grid-Level Storage, Yuhai Hu

Theses and Dissertations

The primary focus of this dissertation is the design, analysis and implementation of stochastic optimal control of grid-level storage. It provides stochastic, quantitative models to aid decision-makers 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 end-of-horizon effects. This is an extension of basic MDP, where the process follows the same pattern every $p$ time periods. We establish improved near-optimality bounds for a class of greedy policies, and derive a corresponding value-iteration algorithm suitable for periodic problems ...


Optimization Algorithms For Machine Learning Designed For Parallel And Distributed Environments, Seyedalireza Yektamaram Jan 2018

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 large-scale machine learning problems. The overall theme is network-based 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 Jan 2018

Computational Methods For Discrete Conic Optimization Problems, Aykut Bulut

Theses and Dissertations

This thesis addresses computational aspects of discrete conic optimization. Westudy two well-known classes of optimization problems closely related to mixedinteger linear optimization problems. The case of mixed integer second-ordercone optimization problems (MISOCP) is a generalization in which therequirement that solutions be in the non-negative orthant is replaced by arequirement that they be in a second-order 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 Jan 2017

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 post-anesthesia 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 Jan 2017

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 Mean-SemiVariance Project (MSVP) portfolio selection problem, where the objective is to obtain the optimal risk-reward portfolio of non-divisible projects when the risk is measured by the semivariance of the portfolio's Net-Present Value (NPV) and the reward is measured by the portfolio's expected NPV. Similar to the well-known Mean-Variance 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 Text-Based Reviews, Sean Byrne Jan 2017

Aspect Identification And Sentiment Analysis In Text-Based Reviews, Sean Byrne

Theses and Dissertations

Online text-based 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 aspect-based 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 Jan 2017

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 Jan 2017

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 well-known quasi-Newton methods such as L-BFGS. 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 Jan 2017

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 non-linear 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 non-linear part for each additional cost assigned to a potential facility site, and a set of ...


Random Models In Nonlinear Optimization, Matthew Joseph Menickelly Jan 2017

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 non-convex) 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 model-based trust-region framework of derivative-free optimization (DFO) for use in situations where objective function values or the set ...


Conic Programming Approaches For Polynomial Optimization: Theory And Applications, Xiaolong Kuang Jan 2017

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 (real-world) 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 non-convex optimization problems, including key combinatorial optimization problems.The focus of the first three ...


Measures Of Information For Information Acquisition Optimization, Zhenglin Wei Jan 2017

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 Jan 2016

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 chal-lenges 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 transmis-sion assumptions.In this thesis a full user-friendly tool in AIMMS was built with4 datasets based ...


A Dea-Based Approach To Evaluate The Efficiency Of Non-Homogeneous Service Locations, Amer Husain Asiri Jan 2016

A Dea-Based Approach To Evaluate The Efficiency Of Non-Homogeneous 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 Jan 2016

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 desk-size area, with a remote-controlled toy car. The software, including the calculation, image processing, and movement control, was coded with Python and C++. Q-learning 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 ...