Industrial & Manufacturing Engineering

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Research from the Department of Industrial & Manufacturing Engineering. The department website may be found at https://www.ndsu.edu/ce/https://www.ndsu.edu/ime/

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Browsing Industrial & Manufacturing Engineering by browse.metadata.program "Industrial Engineering and Management"

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Now showing 1 - 8 of 8
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    Applying Simulation and Genetic Algorithm for Patient Appointment Scheduling Optimization
    (North Dakota State University, 2013) Peng, Yidong
    In this study, we discuss the implementation of integrated simulation and genetic algorithm for patient scheduling optimization under two different settings, namely the "traditional" scheduling system and the "open access" scheduling system. Under the "traditional" setting, we propose a two-phase approach for designing a weekly scheduling template for outpatient clinics providing multiple types of services. Our results demonstrate that the two-phase approach can efficiently find the promising weekly appointment scheduling templates for outpatient clinics. Under the "open access" setting, we propose a discrete event simulation and genetic algorithm (DES-GA) approach to find the heuristic optimal scheduling template for the clinic allowing both open access and walk-in patients. The solution provides scheduling templates consisting of not only the optimal number of reservations for open access appointments and walk-ins, but also the optimized allocation of these reserved slots, by minimizing the average cost per admission of open access or walk-in patient.
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    Challenges and Barriers Affecting the Success of Total Quality Management
    (North Dakota State University, 2013) Rokke, Connie
    This thesis is a compilation of two papers. The first provides an overview of TQM from its beginnings through today's business climate. The fundamental principles of TQM are explored and the benefits identified. A review of the challenges and barriers that prohibit most companies from achieving these successes is conducted to understand why well intentioned companies are not always able to sustain this management technique. The second paper analyzes these challenges and barriers of TQM attempting to quantify their impact on the success of a TQM program. This study analyzes survey data using Structural Equation Modeling. The findings indicate the challenges associated with some of the TQM Principles are correlated and a few of them have an impact on the success of a TQM program. This research is unique in its attempt to apply quantifiable measures to the challenges faced by organizations that endeavor to implement TQM programs.
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    Experimental Study of the Temperature Profile of a Gaussian Type Heat Distribution
    (North Dakota State University, 2020) Mare, Sunny Prashant
    In research aimed at studying the laser microwelding of thermoplastic materials, two theoretical heat flow models were studied by Dr. Grewell. In the surface temperature profile based on the Gaussian type distributed heat source model, two peak temperatures were predicted. The purpose of the current project was to develop a controlled test environment to study the surface temperature profile of a thermoplastic material using a Gaussian type heat source and to validate the prediction of the inflection point in the plastic sample. One of the key results of the initial experimentation was the lack of a Gaussian distribution of the infrared lamp used as a heat source. This limitation was addressed by first employing a thermal camera and then later a laser system. The observation of two peak temperatures was not substantiated with the experimentations conducted in this study.
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    A New Reliability Assessment Model for Power Electronic Modules Considering Failure Mechanism Interaction
    (North Dakota State University, 2015) Zhuang, Xing
    A reliability prediction method is proposed to determine the lifetime of IGBTs (Insulated Gate Bipolar Transistors) under power cycling test based on the performance of solder joint and wire bond. The failure characteristics of solder joint and wire bond are captured via selected PoF model respectively. To provide precise reliability prediction, PoF models are converted into probabilistic models. In addition, the failure interaction between wire bond and solder joint is studied. Wire bond lift-off is treated as the predominant failure mode based upon experiments from literature and solder joint degradation process is triggered by wire bond degradation process. Increased junction temperature is captured as it is affected by the degradation process of both components. In the end, the system reliability is computed in a series system configuration.
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    A Quality Management System Implementation Framework for Small-Sized Companies
    (North Dakota State University, 2016) Sawant, Manish Avinash
    A detailed framework is essential to facilitate quality system implementation. In this study, we have offered a cost effective do-it-yourself approach to quality management. We have proposed a quality system implementation framework for small-medium sized organizations to enable their transition from a no-quality system to an ISO 9001 quality management system. The proposed framework is validated using a case study of a small door manufacturing company. The findings reveal several setbacks experienced during quality system implementation and suggests means to overcome them using a proposed seven step framework. This study also advises an effective maintenance tool to facilitate continuous improvement in organizations after implementing a quality management system. The study results will be useful for quality practitioners, managers, consultants and engineers, especially in small companies and discloses several benefits that can be achieved by employing the proposed framework in any organization irrespective of its size and nature.
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    Resilience Assessment for Complex Networks Based on Recovery Strategies
    (North Dakota State University, 2019) Afrin, Tanzina
    The vulnerability of complex networks to unexpected disruptive events could be reduced by increasing network resilience through the efficient recovery of the damaged network. To find the most efficient recovery strategy among the existing variety of strategies, a resilience-based framework was proposed and implemented for both localized attacks and cascading failures. For localized attacks, preferential recovery based on nodal weights (PRNW), periphery recovery (PR) and localized recovery (LR) were assessed. Additionally, probability-based recovery (RS1) and recovery of neighboring or boundary nodes (RS2) methods were evaluated for cascading failures. Considering the advantages and disadvantages of these strategies, a hybrid recovery strategy was proposed to achieve high network resilience in a timely manner with a manageable amount of cost. Overall, this study aids in the assessment and the development of a cost-effective resilience-based recovery strategy.
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    A Semiparametric Trajectory Model for Cognitive Decline
    (North Dakota State University, 2017) Li, Xiaoxia
    Dementia is a group of diseases that are caused by neurocognitive disorder. It is the second leading cause of death in older adults in the US. People who suffer from dementia experience memory loss and other cognitive or functional decline that is severe enough to interfere with their professional and social performance. In spite of the controversy on accuracy of diagnosis and debate on disclosure of dementia diagnosis results, it is important for patients and their families to know what to expect about the future development of cognitive decline. The course of dementia progression is highly diverse, and the symptoms vary differently from case to case. Amnesia, aphasia, agnosia, and apraxia can exist solely or in combination. The rate of cognitive decline, in the term of Clinical Dementia Rating Score, demonstrated different patterns on an individual level. However, in spite of the variety of symptoms, it is essential to map the cognitive decline to the severity of the impact of the symptoms on daily life. Clinical Dementia Rating SUM score (CDR SUM score) is a comprehensive evaluation based on cognition level. Trajectory modeling can provide a practical tool for physicians to make prognosis and medical trials. Furthermore, trajectory modeling can be a valuable reference for stakeholders to use in reimbursement decisions or policies on caregiving resource allocation. However, there is a gap in the current research to predict the trajectory for cognitive decline. In this research, we studied the typical pattern of CDR SUM scores and predicted a timeline for people with cognitive decline. The innovation and significance of this study is the development of multilevel and semiparametric models, and a simple and straightforward criterion for model evaluation and selection. The model we built showed robustness in both explaining the data and predictions. The study results revealed the factors associated with cognitive decline rate in terms of CDR SUM score, and gave implications on accurate CDR SUM score prediction by individual demographic and clinical profiles. The developed model can also be applied to other longitudinal studies in behavioral science, medical monitoring, and other time series related studies.
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    Two Data Mining Applications for Predicting Pre-Diabetes
    (North Dakota State University, 2015) You, Guangjing
    In this study, the performance of Logistic Regression and Decision Tree modeling is compared by using SAS Enterprise Miner for predicting pre-diabetes in US population by using several of the common factors from the type 2 diabetes screening criteria. From 17 variables of NHANES’ three sets of dataset, a total of 13 risk factors were selected as predictors of pre-diabetes. A comparison of two data mining methodology showed that Decision Tree has a higher ROC index than Logistic Regression modeling. All ROC indexes for two models were greater than 77% indicating both methods present a good prediction for pre-diabetes. The predictive accuracy of the two models was greater than 72% on the whole dataset. Decision tree modeling also resulted in higher accuracy and sensitivity values than Logistic Regression modeling. Taken as a whole, the results of comparison indicated Decision Tree modeling is a better indicator to predict pre-diabetes.