Operations Research, Systems Engineering and Industrial Engineering Commons^™

Statistical Modeling, Learning And Computing For Stochastic Dynamics Of Complex Systems, Mohammadmahdi Hajiha

Graduate Theses and Dissertations

With the recent advances in sensor technology, it is much easier to collect and store streams of system operational and environmental (SOE) data. These data can be used as input to model the underlying behavior of complex engineered systems and phenomenons if appropriate algorithms with well-defined assumptions are developed. This dissertation is comprised of the research work to show the applicability of SOE data when fed into proposed tailored algorithms. The first purposes of these algorithms are to estimate and analyze the reliability of a system as elaborated in Chapter 2. This chapter provides the derivation of closed-form expressions that …

Thin Safety Margin: The Sefor Super-Prompt-Critical Transient Experiments, Ozark Mountains, Arkansas 1970–1971, Jerry Havens, Collis Geren

Arkansas Scholarly Editions

Thin Safety Margin charts the history of SEFOR, a twenty-megawatt reactor that operated for three years in the rural Ozark Mountains of Arkansas as part of an internationally sponsored program designed to demonstrate the Doppler effect in plutonium-oxide-fueled fast reactors. Authors Jerry Havens and Collis Geren draw upon this history to assess the accidental explosion risk inherent in using fast reactors to reduce the energy industry’s carbon dioxide emissions.

If a sufficiently powerful fast-neutron explosion were to cause the containment of a reactor such as SEFOR’s to fail, the reactor’s radiotoxic plutonium fuel could vaporize and escape into the surrounding …

Knowledge Discovery From Complex Event Time Data With Covariates, Samira Karimi

Graduate Theses and Dissertations

In particular engineering applications, such as reliability engineering, complex types of data are encountered which require novel methods of statistical analysis. Handling covariates properly while managing the missing values is a challenging task. These type of issues happen frequently in reliability data analysis. Specifically, accelerated life testing (ALT) data are usually conducted by exposing test units of a product to severer-than-normal conditions to expedite the failure process. The resulting lifetime and/or censoring data are often modeled by a probability distribution along with a life-stress relationship. However, if the probability distribution and life-stress relationship selected cannot adequately describe the underlying failure …

A Machine Learning Approach To Understanding Emerging Markets, Namita Balani

Graduate Theses and Dissertations

Logistic providers have learned to efficiently serve their existing customer bases with optimized routes and transportation resource allocation. The problem arises when there is potential for logistics growth in an emerging market with no previous data. The purpose of this work is to use industry data for previously known and well-documented markets to apply data analytic techniques such as machine learning to investigate the uncertainty in a new market. The thesis looks into machine learning techniques to predict miles per stop given historical data. It mainly focuses on Random Forest Regression Analysis, but concludes that additional techniques, such as Polynomial …

Improving Logistics Efficiency Through Collaborative Truck Routing, Patrick Dougherty

Industrial Engineering Undergraduate Honors Theses

The logistics industry is among the world’s largest and most essential. Specifically, trucking is a massive component of the modern logistics system. In 2012, truck transportation carried 68% of all freight tonnage in the U.S. However, trucking currently faces significant problems with efficiency and sustainability. Of all miles driven by trucks yearly in the U.S., 25% are driven with empty loads and 36% are driven with underutilized loads. In addition to this economic inefficiency, the industry faces social and environmental challenges. Driver turnover rates are near 100%, and trucking accounts for a significant level of greenhouse gas emissions. One potential …

Scheduling Allocation And Inventory Replenishment Problems Under Uncertainty: Applications In Managing Electric Vehicle And Drone Battery Swap Stations, Amin Asadi

Graduate Theses and Dissertations

In this dissertation, motivated by electric vehicle (EV) and drone application growth, we propose novel optimization problems and solution techniques for managing the operations at EV and drone battery swap stations. In Chapter 2, we introduce a novel class of stochastic scheduling allocation and inventory replenishment problems (SAIRP), which determines the recharging, discharging, and replacement decisions at a swap station over time to maximize the expected total profit. We use Markov Decision Process (MDP) to model SAIRPs facing uncertain demands, varying costs, and battery degradation. Considering battery degradation is crucial as it relaxes the assumption that charging/discharging batteries do not …