Enabling Non-Expert Sustainable Manufacturing Process And Supply Chain Analysis During The Early Product Design Phase, 2017 Oregon State University
Enabling Non-Expert Sustainable Manufacturing Process And Supply Chain Analysis During The Early Product Design Phase, Kamyar Raoufi, Karl R. Haapala, Kathy L. Jackson, Kyoung-Yun Kim, Gul E. Okudan-Kremer, Carolyn E. Psenka
Consumers are pressuring companies to produce products with superior sustainability performance, yet educators are disadvantaged in training students about sustainable engineering and many engineers are often not well-positioned to perform product sustainability assessments. In particular, quantifying environmental impacts is a key aspect of achieving improved product sustainability performance that has garnered much attention over the past two decades, but tools remain deficient to assist manufacturing decision making. In light of efforts undertaken to develop sustainability assessment methodologies, we review recent developments in quantifying a widely adopted environmental performance metric, carbon footprint, in manufacturing processes and supply chain networks. We also ...
Systematic Design For Trait Introgression Projects, 2017 Iowa State University
Systematic Design For Trait Introgression Projects, John N. Cameron, Ye Han, Lizhi Wang, William D. Beavis
We demonstrate an innovative approach for designing Trait Introgression (TI) projects based on optimization principles from Operations Research. If the designs of TI projects are based on clear and measurable objectives, they can be translated into mathematical models with decision variables and constraints that can be translated into Pareto optimality plots associated with any arbitrary selection strategy. The Pareto plots can be used to make rational decisions concerning the trade-offs between maximizing the probability of success while minimizing costs and time. The systematic rigor associated with a cost, time and probability of success (CTP) framework is well suited to designing ...
Graduated Stress Exposure Of Spaceflight Hazards In A Virtual Environment, 2017 Iowa State University
Graduated Stress Exposure Of Spaceflight Hazards In A Virtual Environment, Tor Finseth, Nir Keren, Warren D. Franke, Michael C. Dorneich, Clayton C. Anderson
Warren D Franke
Stress experienced by astronauts during high-level hazardous situations may poses risk to personnel wellbeing and to mission success. Stress inoculation training (SIT) provides individuals with experience of minor stressors and coping skills during non-critical times to enhance their resistance to stress. This study evaluates the effect of exposure to a low level stressor on physiological response and cognitive load in high level stressor setting. Simulation of fire emergency on the International Space Station (ISS) in a full-scale, immersive, interactive, 3D virtual reality environment facilitated a process for stress inoculation. The experimental settings included two groups that have been exposed to ...
Incorporation Of Future Building Operating Conditions Into The Modeling Of Building–Microclimate Interaction: A Feasibility Approach, Kelly Kalvelage, Ulrike Passe, Caroline Krejci, Michael C. Dorneich
This paper presents a novel modeling methodology that integrates the near building environmental conditions (or microclimate), whole-building design, and occupant behavior. Accurate predictions of the future building operating conditions lead to designs that serve the building’s purpose – to support occupants’ tasks. This study bridges the gap between human factors and architecture to include physical, cognitive, and organizational systems into building information modeling using future typical meteorological year climate data, canyon air temperature microclimate model, and a whole-building energy simulation to investigate the impact of future microclimate conditions on a “typical” single-occupant office. Additionally, to capture the effects of building ...
Risk-Averse Stochastic Integer Programs For Mixed-Model Assembly Line Sequencing Problems, 2017 Iowa State University
Risk-Averse Stochastic Integer Programs For Mixed-Model Assembly Line Sequencing Problems, Ge Guo, Sarah M. Ryan
Sarah M. Ryan
A variety of optimization formulations have been proposed for mixed-model assembly sequencing problems with stochastic demand and task times. In the real world, however, mixed-model assembly lines are faced with more challenging uncertainties including timely part delivery, material quality, upstream sub-assembly completion and availability of other resources. In addition, sub-assembly lines must meet deadlines imposed by downstream stations. The inevitable disruptions require resequencing. We present a risk-averse stochastic mixed-integer model for mixed-model assembly line resequencing problems to increase on-time performance.
Utilizing Six Sigma Methodology For Training Undergraduate Student For Conducting Global Field Research, 2017 Iowa State University
Utilizing Six Sigma Methodology For Training Undergraduate Student For Conducting Global Field Research, Kritika Chopra, Shweta Chopra, Chad Laux
With the increase in demand for the global research, scholars in engineering and technology discipline do not hesitate in taking up global opportunity for conducting research. Training the next generation for such international research opportunity is key and involving undergraduate students’ beyond study abroad is important.
A Pseudo-Likelihood Analysis For Incomplete Warranty Data With A Time Usage Rate Variable And Production Counts, 2017 Iowa State University
A Pseudo-Likelihood Analysis For Incomplete Warranty Data With A Time Usage Rate Variable And Production Counts, Yu Qiu, Danial J. Nordman, Stephen B. Vardeman
Stephen B. Vardeman
The most direct purpose of collecting warranty data is tracking associated costs. However, they are also useful for quantifying a relationship between use rate and product time-to-first-failure and for estimating the distribution of product time-to-first-failure (which is modeled in this article as depending on use rate and a unit potential life length under continuous use). Employing warranty data for such reliability analysis purposes is typically complicated by the fact that some parts of some warranty data records are missing. A pseudo-likelihood methodology is introduced to deal with some kinds of incomplete warranty data (such as that available in a motivating ...
One-Sample Bayes Inference For Symmetric Distributions Of 3-D Rotations, 2017 Iowa State University
One-Sample Bayes Inference For Symmetric Distributions Of 3-D Rotations, Yu Qiu, Danial J. Nordman, Stephen B. Vardeman
Stephen B. Vardeman
A variety of existing symmetric parametric models for 3-D rotations found in both statistical and materials science literatures are considered from the point of view of the “uniform-axis-random-spin” (UARS) construction. One-sample Bayes methods for non-informative priors are provided for all of these models and attractive frequentist properties for corresponding Bayes inference on the model parameters are confirmed. Taken together with earlier work, the broad efficacy of non-informative Bayes inference for symmetric distributions on 3-D rotations is conclusively demonstrated.
Modern Measurement, Probability, And Statistics: Some Generalities And Multivariate Illustrations, 2017 Iowa State University
Modern Measurement, Probability, And Statistics: Some Generalities And Multivariate Illustrations, Stephen B. Vardeman
Stephen B. Vardeman
In broad terms, effective probability modeling of modern measurement requires the development of (usually parametric) distributions for increasingly complex multivariate outcomes driven by the physical realities of particular measurement technologies. “Differences” between measures of distribution center and truth function as “bias.” Model features that allow hierarchical compounding of variation function to describe “variance components” like “repeatability,” “reproducibility,” “batch-to-batch variation,” etc. Mixture features in models allow for description (and subsequent downweighting) of outliers. For a variety of reasons (including high-dimensionality of parameter spaces relative to typical sample sizes, the ability to directly include “Type B” considerations in assessing uncertainty, and the ...
Bayes Inference For A Tractable New Class Of Non-Symmetric Distributions For 3-Dimensional Rotations, 2017 University of Wisconsin - La Crosse
Bayes Inference For A Tractable New Class Of Non-Symmetric Distributions For 3-Dimensional Rotations, Melissa Ann Bingham, Danial J. Nordman, Stephen B. Vardeman
Stephen B. Vardeman
Both existing models for non-symmetric distributions on 3-dimensional rotations and their associated one-sample inference methods have serious limitations in terms of both interpretability and ease of use. Based on the intuitively appealing Uniform Axis- Random Spin (UARS) construction of Bingham, Nordman, and Vardeman (2009) for symmetric families of distributions, we propose new highly interpretable and tractable classes of non-symmetric distributions that are derived from mixing UARS distributions. These have an appealing Preferred Axis-Random Spin (PARS) construction and (unlike existing models) directly interpretable parameters. Non-informative one-sample Bayes inference in these models is a direct generalization of UARS methods introduced in Bingham ...
Elementary Statistical Methods And Measurement Error, 2017 Iowa State University
Elementary Statistical Methods And Measurement Error, Stephen B. Vardeman, Joanne Wendelberger, Tom Burr, Michael S. Hamada, Leslie M. Moore, Marcus Jobe, Max Morris, Huaiqing Wu
Stephen B. Vardeman
How the sources of physical variation interact with a data collection plan determines what can be learned from the resulting dataset, and in particular, how measurement error is reflected in the dataset. The implications of this fact are rarely given much attention in most statistics courses. Even the most elementary statistical methods have their practical effectiveness limited by measurement variation; and understanding how measurement variation interacts with data collection and the methods is helpful in quantifying the nature of measurement error. We illustrate how simple one- and two-sample statistical methods can be effectively used in introducing important concepts of metrology ...
Majority Voting By Independent Classifiers Can Increase Error Rates, 2017 Iowa State University
Majority Voting By Independent Classifiers Can Increase Error Rates, Stephen B. Vardeman, Max Morris
Stephen B. Vardeman
The technique of “majority voting” of classifiers is used in machine learning with the aim of constructing a new combined classification rule that has better characteristics than any of a given set of rules. The “Condorcet Jury Theorem” is often cited, incorrectly, as support for a claim that this practice leads to an improved classifier (i.e., one with smaller error probabilities) when the given classifiers are sufficiently good and are uncorrelated. We specifically address the case of two-category classification, and argue that a correct claim can be made for independent (not just uncorrelated) classification errors (not the classifiers themselves ...
Life Cycle Assessment Of The Production Of Hydrogen And Transportation Fuels From Corn Stover Via Fast Pyrolysis, 2017 Iowa State University
Life Cycle Assessment Of The Production Of Hydrogen And Transportation Fuels From Corn Stover Via Fast Pyrolysis, Yanan Zhang, Guiping Hu, Robert C. Brown
Robert C. Brown
This life cycle assessment evaluates and quantifies the environmental impacts of the production of hydrogen and transportation fuels from the fast pyrolysis and upgrading of corn stover. Input data for this analysis come from Aspen Plus modeling, a GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) model database and a US Life Cycle Inventory Database. SimaPro 7.3 software is employed to estimate the environmental impacts. The results indicate that the net fossil energy input is 0.25 MJ and 0.23 MJ per km traveled for a light-duty vehicle fueled by gasoline and diesel fuel, respectively. Bio-oil ...
Capturing Cognitive Fingerprints From Keystroke Dynamics, 2017 Iowa State University
Capturing Cognitive Fingerprints From Keystroke Dynamics, J. Morris Chang, Chi-Chen Fang, Kuan-Hsing Ho, Norene Kelly, Pei-Yuan Wu, Yixiao Ding, Chris Chu, Stephen B. Gilbert, Amed E. Kamal, Sun-Yuan Kung
Conventional authentication systems identify a user only at the entry point. Keystroke dynamics can continuously authenticate users by their typing rhythms without extra devices. This article presents a new feature called cognitive typing rhythm (CTR) to continuously verify the identities of computer users. Two machine techniques, SVM and KRR, have been developed for the system. The best results from experiments conducted with 1,977 users show a false-rejection rate of 0.7 percent and a false-acceptance rate of 5.5 percent. CTR therefore constitutes a cognitive fingerprint for continuous. Its effectiveness has been verified through a large-scale dataset. This article ...
Scenario Reduction For Stochastic Unit Commitment With Wind Penetration, 2017 Iowa State University
Scenario Reduction For Stochastic Unit Commitment With Wind Penetration, Yonghan Feng, Sarah M. Ryan
Sarah M. Ryan
Uncertainties in the day-ahead forecasts for load and wind energy availability are considered in a reliability unit commitment problem. A two-stage stochastic program is formulated to minimize total expected cost, where commitments of thermal units are viewed as first-stage decisions and dispatch is relegated to the second stage. Scenario paths of hourly loads are generated according to a weather forecast-based load model. Wind energy scenarios are obtained by identifying analogue historical days. Net load scenarios are then created by crossing scenarios from each set and subtracting wind energy from load. A new heuristic scenario reduction method termed forward selection in ...
Demand Price Sensitivity And Market Power In A Congested Fuel And Electricity Network, 2017 Iowa State University
Demand Price Sensitivity And Market Power In A Congested Fuel And Electricity Network, Sarah M. Ryan
Sarah M. Ryan
IN restructured electricity markets, generating companies submit bids to supply electricity at prices based on their marginal costs, which are driven largely by fuel costs. In each regional wholesale power market, an independent system operator (ISO) manages electricity transmission and sets locational marginal prices (LMPs) to match supplies with demands at each location on the constrained grid. To understand the interaction between constraints on fuel supply and constraints on electricity transmission, we recently developed and tested a game theoretic model that combines both sets of constraints . It includes (1) costs of extracting and transporting finite supplies of fuels ...
Capacity Expansion For A Loss System With Exponential Demand Growth, 2017 Sabritec, Inc.
Capacity Expansion For A Loss System With Exponential Demand Growth, Alexander Simampo, Sarah M. Ryan
Sarah M. Ryan
We study a loss system to forecast the demand for capacity based on the forecast demand for service and a specified service level. A little-used property of the Erlang loss formula allows the linear transformation of demand for service into demand for capacity. Next, given the forecast demand for capacity, we approximate a long-run optimal capacity expansion policy by optimizing over successively longer finite time horizons. Analytical formulas together with regression analysis show the significance of the number of potential customers, frequency and duration of their requests for service, and the specified service level on the demand for capacity. Numerical ...
Capacity Expansion With Lead Times And Autocorrelated Random Demand, 2017 Iowa State University
Capacity Expansion With Lead Times And Autocorrelated Random Demand, Sarah M. Ryan
Sarah M. Ryan
The combination of uncertain demand and lead times for installing capacity creates the risk of shortage during the lead time, which may have serious consequences for a service provider. This paper analyzes a model of capacity expansion with autocorrelated random demand and a fixed lead time for adding capacity. To provide a specified level of service, a discrete time expansion timing policy uses a forecast error-adjusted minimum threshold level of excess capacity position to trigger an expansion. Under this timing policy, the expansion cost can be minimized by solving a deterministic dynamic program. We study the effects of demand characteristics ...
A New Approximation Method For Generating Day-Ahead Load Scenarios, 2017 Iowa State University
A New Approximation Method For Generating Day-Ahead Load Scenarios, Yonghan Feng, Dinakar Gade, Sarah M. Ryan, Jean-Paul Watson, Roger J.B. Wets, David L. Woodruff
Sarah M. Ryan
Unit commitment decisions made in the day-ahead market and resource adequacy assessment processes are based on forecasts of load, which depends strongly on weather. Two major sources of uncertainty in the load forecast are the errors in the day-ahead weather forecast and the variability in temporal patterns of electricity demand that is not explained by weather. We develop a stochastic model for hourly load on a given day, within a segment of similar days, based on a weather forecast available on the previous day. Identification of similar days in the past is based on weather forecasts and temporal load patterns ...
Determining Inventory Levels In A Conwip Controlled Job Shop, 2017 Iowa State University
Determining Inventory Levels In A Conwip Controlled Job Shop, Sarah M. Ryan, Bruno Baynat, F. Fred Choobineh
Sarah M. Ryan
We extend the concept of CONWIP control to a job shop setting, in which multiple products with distinct routings compete for the same set of resources. The problem is to determine the fixed overall WIP level and its allocation to product types (WIP mix) to meet a uniformly high customer service requirement for each product type. We formulate an optimization problem for an open queuing network model in which customer orders pull completed products from the system. Then, assuming heavy demand, we derive a throughput target for each product type in a closed queuing network and provide a simple heuristic ...