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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Adaptive Production Planning and Scheduling for the Make-to-order DNA Manufacturing System
(North Dakota State University, 2010) Song, Dan
This thesis develops an adaptive production planning and scheduling system for the make-to-order plasmid (DNA) manufacturing system. The system, which has stochastic nature and random demand, was represented by a mathematical programming model first. Then in order to solve it, discrete-event simulation models were developed to generate a feasible schedule that maximizes the production throughput in the planning horizon in a mix-product type environment. A special heuristic order selecting and splitting procedure was designed to aid the production planning and scheduling process. Experiments were conducted to evaluate the algorithm and results are compared with those obtained by using four classic dispatching rules, such as first come first served (FCFS) and shortest processing time (SPT). To take advantage of simulation results, a rule-based expert system was created with pre-defined scheduling rules. Rules regarding production planning and scheduling can be used by human schedulers easily and the system is very flexible in further extension.
Advanced Numerical Modeling in Manufacturing Processes
(North Dakota State University, 2022) Dey, Arup
In manufacturing applications, a large number of data can be collected by experimental studies and/or sensors. This collected data is vital to improving process efficiency, scheduling maintenance activities, and predicting target variables. This dissertation explores a wide range of numerical modeling techniques that use data for manufacturing applications. Ignorance of uncertainty and the physical principle of a system are shortcomings of the existing methods. Besides, different methods are proposed to overcome the shortcomings by incorporating uncertainty and physics-based knowledge. In the first part of this dissertation, artificial neural networks (ANNs) are applied to develop a functional relationship between input and target variables and process parameter optimization. The second part evaluates the robust response surface optimization (RRSO) to quantify different sources of uncertainty in numerical analysis. Additionally, a framework based on the Bayesian network (BN) approach is proposed to support decision-making. Due to various uncertainties, estimating interval and probability distribution are often more helpful than deterministic point value estimation. Thus, the Monte Carlo (MC) dropout-based interval prediction technique is explored in the third part of this dissertation. A conservative interval prediction technique for the linear and polynomial regression model is also developed using linear optimization. Applications of different data-driven methods in manufacturing are useful to analyze situations, gain insights, and make essential decisions. But, the prediction by data-driven methods may be physically inconsistent. Thus, in the fourth part of this dissertation, a physics-informed machine learning (PIML) technique is proposed to incorporate physics-based knowledge with collected data for improving prediction accuracy and generating physically consistent outcomes. Each numerical analysis section is presented with case studies that involve conventional or additive manufacturing applications. Based on various case studies carried out, it can be concluded that advanced numerical modeling methods are essential to be incorporated in manufacturing applications to gain advantages in the era of Industry 4.0 and Industry 5.0. Although the case study for the advanced numerical modeling proposed in this dissertation is only presented in manufacturing-related applications, the methods presented in this dissertation is not exhaustive to manufacturing application and can also be expanded to other data-driven engineering and system applications.
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.
Assessing Reliability of Highly Reliable Products Using Accelerated Degradation Test Design, Modeling, and Bayesian Inference
(North Dakota State University, 2019) Limon, Shah Mohammad
The accelerated degradation test methods have proven to be a very effective approach to quickly evaluate the reliability of highly reliable products. However, the modeling of accelerated degradation test data to estimate reliability at normal operating condition is still a challenging task especially in the presence of multi-stress factors. In this study, a nonstationary gamma process is considered to model the degradation behavior assuming the strict monotonicity and non-negative nature of the product deterioration. It further assumes that both the gamma process parameters are stress dependent. A maximum likelihood method has been used for the model parameter estimation. The case study results indicate that traditional models that assume only shape parameter as stress dependent underestimate the product reliability significantly at normal operating conditions. This study further revealed that the scale parameter at a higher stress level is very close to the traditional constant assumption. However, at the normal operating condition, scale parameter value differs significantly with the traditional constant assumption value. This difference leads to the larger difference of reliability and lifetime estimates provided by the proposed approach. A Monte Carlo simulation with the Bayesian updating method has been incorporated to update the gamma parameters and reliability estimates when additional degradation data become available. A generalized reliability estimation framework for using the ADT data is also presented in this work. Further, in this work, an optimal constant-stress accelerated degradation test plan is presented considering the gamma process. The optimization criteria are set by minimizing the asymptotic variance of the maximum likelihood estimator of the lifetime at operating condition under total experimental cost constraint. A heuristic based more specifically genetic algorithm approach has been implemented to solve the model. Additionally, a sensitivity analysis is performed which revealed that increasing budget causes longer test duration time with smaller sample size. Also, it reduces the asymptotic variance of the estimation which is very intuitive as more budget increase the possibility to generate more degradation information and helps to increase the estimation accuracy. The overall reliability assessment methodology and the test design has been demonstrated using the carbon-film resistor degradation data.
CFD Heat Transfer Simulation of the Human Upper Respiratory Tract for Oronasal Breathing Condition
(North Dakota State University, 2011) Srinivasan, Raghavan
In this thesis. a three dimensional heat transfer model of heated airflow through the upper human respiratory tract consisting of nasal, oral, trachea, and the first two generations of bronchi is developed using computational fluid dynamics simulation software. Various studies have been carried out in the literature investigating the heat and mass transfer characteristics in the upper human respiratory tract, and the study focuses on assessing the injury taking place in the upper human respiratory tract and identifying acute tissue damage based on level of exposure. The model considered is for the simultaneous oronasal breathing during the inspiration phase with high volumetric flow rate of 90/liters minute and a surrounding air temperature of 100 degrees centigrade. The study of the heat and mass transfer, aerosol deposition and flow characteristics in the upper human respiratory tract using computational fluid mechanics simulation requires access to a two dimensional or three dimensional model for the human respiratory tract. Depicting an exact model is a complex task since it involves the prolonged use of imaging devices on the human body. Hence a three dimensional geometric representation of the human upper respiratory tract is developed consisting of nasal cavity, oral cavity, nasopharynx, pharynx, oropharynx, trachea and first two generations of the bronchi. The respiratory tract is modeled circular in cross-section and varying diameter for various portions as identified in this study. The dimensions are referenced from the literature herein. Based on the dimensions, a simplified model representing the human upper respiratory tract is generated.This model will be useful in studying the flow characteristics and could assist in treatment of injuries to the human respiratory tract as well as help optimize drug delivery mechanism and dosages. Also a methodology is proposed to measure the characteristic dimension of the human nasal and oral cavity at the inlet/outlet points which are classified as internal measurements.
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.
Design and Manufacturing of Variable Stiffness Cellular Architecture
(North Dakota State University, 2017) Xie, Ruinan
Cellular structures are highly evaluated due to their high material efficiency. Both theoretical and experimental studies have done on periodic cellular structures. However, the mechanical performance can be stochastically distributed in the cellular architecture. This thesis presents the design and manufacturing of variable stiffness cellular architecture to achieve optimized topology by changing the unit cell parameters. The author applies image analysis technique to extract and digitize the information from the performance distribution map. Two types of cellular cells are studied for their relationship of stiffness and relative density. The methods of voxelization for both cells are also given in this study. This proposed methodology is then implemented to design a customized mattress and compare with current existing mattress. With the study of the unit cells and voxelization technique, our designed mattress aligns body curve better which provides more recuperation of the body during sleep.
Designing Bio-Ink for Extrusion Based Bio-Printing Process
(North Dakota State University, 2019) Habib, MD Ahasan
Tissue regeneration using in-vitro scaffold becomes a vital mean to mimic the in-vivo counterpart due to the insufficiency of animal models to predict the applicability of drug and other physiological behavior. Three-dimensional (3D) bio-printing is an emerging technology to reproduce living tissue through controlled allocation of biomaterial and cell. Due to its bio-compatibility, natural hydrogels are commonly considered as the scaffold material in bio-printing process. However, repeatable scaffold structure with good printability and shape fidelity is a challenge with hydrogel material due to weak bonding in polymer chain. Additionally, there are intrinsic limitations for bio-printing of hydrogels due to limited cell proliferation and colonization while cells are immobilized within hydrogels and don’t spread, stretch and migrate to generate new tissue. The goal of this research is to develop a bio-ink suitable for extrusion-based bio-printing process to construct 3D scaffold. In this research, a novel hybrid hydrogel, is designed and systematic quantitative characterization are conducted to validate its printability, shape fidelity and cell viability. The outcomes are measured and quantified which demonstrate the favorable printability and shape fidelity of our proposed material. The research focuses on factors associated with pre-printing, printing and post-printing behavior of bio-ink and their biology. With the proposed hybrid hydrogel, 2 cm tall acellular 3D scaffold is fabricated with proper shape fidelity. Cell viability of the proposed material are tested with multiple cell lines i.e. BxPC3, prostate stem cancer cell, HEK 293, and Porc1 cell and about 90% viability after 15-day incubation have been achieved. The designed hybrid hydrogel demonstrate excellent behavior as bio-ink for bio-printing process which can reproduce scaffold with proper printability, shape fidelity and higher cell survivability. Additionally, the outlined characterization techniques proposed here open-up a novel avenue for quantifiable bio-ink assessment framework in lieu of their qualitative evaluation.
A Domain-Knowledge Modeling of Hospital-Acquired Infection Risk in Healthcare Personnel From Retrospective Observational Data: A Case Study for Covid-19
(North Dakota State University, 2022) Huynh, Phat
Healthcare personnel (HCP) is facing a consistent risk of viral infections. We proposed a domain-knowledge-driven infection risk model to quantify the individual HCP and the population-level risks. For individual-level risk estimation, a time-variant model was proposed to capture the disease transmission dynamics. At the population-level, the infection risk was estimated using a Bayesian network model constructed from three feature sets. For model validation, we investigated the case study of the Coronavirus disease. The variance-based sensitivity analysis indicated that the uncertainty in the estimated risk was attributed to two variables: the number of close contacts and the viral transmission probability. We further validated the individual risk model by considering six occupations in the U.S. O*Net database. For the population-level risk model validation, the infection risk in Texas and California was estimated. The accurate estimation of infection risk will significantly enhance the PPE allocation, safety plans for HCP, and hospital staffing strategies.
Dynamic Pricing in Supply Chains Bringing the Perishable Approach to Dynamic Car Market
(North Dakota State University, 2014) Tripathi, Prateek
In a business environment, using dynamic pricing is a standard practice, especially in the management of revenue. Given the availability of online information concerning inventory and pricing, customers are in a position to understand pricing strategies that sellers employ, and at the same time to be able to develop a possible response strategy. In this thesis, Dynamic Pricing in the Supply Chain: Bringing the Perishable Approach to Dynamic Car Market is investigated and evaluated. This study incorporates strategic consumer response to dynamic prices, particularly for perishable goods, using a number of variables, such as income, demand and price. The main factors that influence stochastic behavior of prices in car market supply chains are the focus of the analysis. It also includes the appropriate parameters to include in a dynamic optimization-pricing supply chain problem and a discussion of how businesses can efficiently optimize the pricing problem in a stochastic market situation.
Economic Analysis of Packaging Systems
(North Dakota State University, 2011) Biradar, Vaibhav Mahadev
Packaging has a significant impact on the efficiency and effectiveness of the supply chain, where improvement can be achieved through the development and selection of an appropriate packaging system. One way to explore this is through the development and use of mathematical models that facilitate economic analysis of packaging systems. Recently, one of the most remarkable trends in logistics is the extensive use of returnable or reusable containers. Returnable container systems have increasingly been introduced in various industries to take advantages of cost savings, but it is very crucial to ensure that a reusable packaging system is an economical packaging choice. In this thesis, an extensive study of an economic analysis of disposable, recyclable, and reusable packaging systems is conducted. This includes identification of significant cost factors and variables involved in the management of disposable, recyclable and reusable packaging systems, and formulation of a mathematical model to compare total cost of packaging systems. The developed mathematical model can be used to choose the most economical packaging system for industries. The linear programming (LP) method is used to develop the mathematical model. The various new factors such as the collapsible ratio of recyclable, disposable and reusable packages have been introduced for the first time in the economic analysis of the packaging systems. The developed mathematical model can be used for a range of industries and for different industry scenarios. The packaging system information of Toyota assembly plant is used for the validation of a mathematical model. The obtained results are compared with previous research based on the same data set and results found in concert with the finding of previous research which validate the model.
The Effect of Stress on Task Capacity and Situational Awareness
(North Dakota State University, 2012) Karim, Reza Ul
In today’s industry, many occupations require manpower resources to include both labor and cognitive resources. As the technology is rapidly changing and businesses are becoming more dependent on cognitive performance, it is essential to find any effect physical stress might have on task performance. Situational awareness is also becoming an integral part of human task performance. It is critical for many operations to design systems such that the effects of physical stress, however minute, on task performance and situational awareness are considered. The test methodology developed here measures the effect of stress on cognitive task performance as a result of situational awareness related to the task. The test measured and compared task capacity among different age groups and different working groups. A comparison was made on task performance based on the effects of low level physical stress and lack of it. Response time and accuracy were measured for statistical analysis. The subject’s stress levels were measured before starting the test to create a baseline for the candidates stress level. The developed tool was able to detect the effect of stress on task performance successfully and efficiently. Subjects with previous work experience performed better both in Phase I and Phase II of the experiment as compared to subjects with no previous work experience. The analysis indicates low level stress does have significant effects on task performance. In reality, stress is an unavoidable factor in daily activities. When designing any system that requires cognitive tasks, stress needs to be considered as a contributing factor to the variability of operation.
Electrical Performance Analysis of a Novel Embedded Chip Technology
(North Dakota State University, 2012) Sarwar, Ferdous
Recently ultra-thin embedded die technology gained much attention for their reduced footprint, light weight, conformality and three-dimensional assembly capabilities. The traditional flexible circuit fabrication process showed its limitations to meet the demand for increasing packaging density. The embedded die technology can be successfully utilized to develop flexible printed circuits that will satisfy the demand for reliable and high density packaging. With a tremendous application potential in wearable and disposable electronics, the reliability of the flexible embedded die package is of paramount importance. Presented is the author's contribution to the novel fabrication process for flexible packages with ultrathin (below 50 µm) dice embedded into organic polymer substrate and the results from the investigation of the electrical performance of embedded bare dice bumped using three different techniques. In this research, embedded flexible microelectronic packaging technology was developed and reliability of different packages was evaluated through JEDEC test standards based on their electrical performance. The reliability test of the developed packages suggested the better and stable performance of stud bump bonded packages. This research also covered the thinning and handling ultra-thin chips, die metallization, stud bump formation, laser ablation of polymers, and assembly of ultra-thin die. The stud bumped flexible packages that were designed and developed in this research have promising application potential in wearable RFID tags, smart textile and three dimensional-stacked packaging, among the many other application areas.
Evacuation Trees with Contraflow and Divergence Considerations
(North Dakota State University, 2018) Achrekar, Omkar Shirish
In this thesis, we investigate how to evacuate people using the available road transportation network efficiently. To successfully do that, we need to design evacuation model that is fast, safe, and seamless. We enable the first two criteria by developing a macroscopic, time-dynamic evacuation model that aims to maximize the number of people in relatively safer areas of the network at each time point; the third criterion is optimized by constructing an evacuation tree, where the vehicles are evacuated using a single path to safety. Divergence and contraflow policies have been incorporated to enhance the network capacity. Divergence enables specific nodes to diverge their flows into two or more streets, while contraflow allows certain streets to reverse their flow, effectively increasing their capacity. We investigate the performance of these policies in the evacuation networks obtained, and present results on two benchmark networks of Sioux Falls and Chicago.
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.
Form and Functionality of Additively Manufactured Parts with Internal Structure
(North Dakota State University, 2019) Ahsan, AMM Nazmul
The tool-less additive manufacturing (AM) or 3D printing processes (3DP) use incremental consolidation of feed-stock materials to construct part. The layer by layer AM processes can achieve spatial material distribution and desired microstructure pattern with high resolution. This unique characteristics of AM can bring custom-made form and tailored functionality within the same object. However, incorporating form and functionality has their own challenge in both design and manufacturing domain. This research focuses on designing manufacturable topology by marrying form and functionality in additively manufactured part using infill structure. To realize the goal, this thesis presents a systematic design framework that focuses on reducing the gap between design and manufacturing of complex architecture. The objective is to develop a design methodology of lattice infill and thin shell structure suitable for additive manufacturing processes. Particularly, custom algorithmic approaches have been developed to adapt the existing porous structural patterns for both interior and exterior of objects considering application specific functionality requirements. The object segmentation and shell perforation methodology proposed in this work ensures manufacturability of large scale thin shell or hollowed objects and incorporates tailored part functionality. Furthermore, a computational design framework developed for tissue scaffold structures incorporates the actual structural heterogeneity of natural bones obtained from their medical images to facilitate the tissue regeneration process. The manufacturability is considered in the design process and the performances are measured after their fabrication. Thus, the present thesis demonstrates how the form of porous structures can be adapted to mingle with functionality requirements of the application as well as fabrication constraints. Also, this work bridges the design framework (virtual) and the manufacturing platform (realization) through intelligent data management which facilitates smooth transition of information between the two ends.
Impact of Integrating Zone Bypass Conveyor on the Performance of a Pick-To-Light Order Picking System
(North Dakota State University, 2012) Xu, Xia
This thesis investigates the impact of integrating Zone Bypass (ZBP) conveyor to a Pick-To-Light (PTL) order picking system. This integration results in a new system (PTL+Z), which could be helpful to achieve higher levels of productivity in warehousing operations. Two options have been proposed to improve the current PTL system productivity. One is to adapt the ZBP conveyor, which will help each order to bypass unnecessary zones with nothing to pick. Another one is to better plan stock keeping units (SKU) assignment by applying level loading assignment. Mathematical models are developed to evaluate system throughput of PTL system with random assignment (PTL/R), PTL system with level loading assignment (PTL/L), PTL+Z system with random assignment (PTL+Z/R), and PTL+Z system with level loading assignment (PTL+Z/L). Simulation models are validated to test the reliability of mathematical models. Also, economic analysis is developed in term of payback period for decision purpose.
Implementing Industry 4.0: a study of socio-technical readiness among manufacturers in Minnesota and North Dakota
(North Dakota State University, 2024) Roth, Katherine
The implementation of Industry 4.0 has become increasingly prevalent in the manufacturing industry since its inception. With the introduction of these newer technologies, changes in personnel and organizational structures occur. The purposeful joint optimization of social and technical factors of organizations is imperative to the successful adoption of Industry 4.0. Thus, the socio-technical system theory addresses a holistic design of human, technology, and organization subsystems of the manufacturing process and their interdependencies. This dissertation investigates the progress made towards implementing Industry 4.0 by small, medium, and large manufacturers in Minnesota and North Dakota. The outcomes of two surveys conducted among a group in Minnesota and North Dakota are analyzed and the results are compared to national and international data. This research identifies potential challenges, as well as, advantages in the current socio-economic landscape for manufacturers that may be either impeding or encouraging the development of a competitive and sustainable manufacturing business. As well, the implementation of flexible work arrangements in the modern work environment has increased in recent years. The first survey posed questions based on a socio-technical theory framework, Industry 4.0, and productivity outcomes. Insights were provided as to how regional manufacturers were utilizing the socio-technical design framework to integrate Industry 4.0 into the organizational design and extract value, such as increased productivity. The joint optimization of social and technical factors within an organization is necessary for the successful adoption of hybrid work environments. The outcomes of the second survey conducted among a group of small, medium, and large manufacturers in Minnesota and North Dakota were assessed the level of socio-technical readiness among regional manufacturers. The survey posed questions based on socio-technical design, digital maturity, organizational learning, responsible autonomy, leadership, communication strategies, and reduced work week schedules. Insights were provided as to how these critical factors support sustainability initiatives, such as reduced work week schedules. As a result of the surveys, a socio-technical strengths, weaknesses, opportunities, and threats (SWOT) analysis framework to complete was proposed to guide the organization through the Industry 4.0 implementation process, assess opportunities for the reduction of work hours, and facilitate the strategic enterprise-wide buy-in from employees and diverse stakeholders.
Improvement of Wind Forecasting Accuracy and its Impacts on Bidding Strategy Optimization for Wind Generation Companies
(North Dakota State University, 2012) Li, Gong
One major issue of wind generation is its intermittence and uncertainty due to the highly volatile nature of wind resource, and it affects both the economy and the operation of the wind farms and the distribution networks. It is thus urgently needed to develop modeling methods for accurate and reliable forecasts on wind power generation. Meanwhile, along with the ongoing electricity market deregulation and liberalization, wind energy is expected to be directly auctioned in the wholesale market. This brings the wind generation companies another issue of particular importance, i.e., how to maximize the profits by optimizing the bids in the gradually deregulated electricity market based on the improved wind forecasts. As such, the main objective of this dissertation research is to investigate and develop reliable modeling methods for tackling the two issues. To reach the objective, three main research tasks are identified and accomplished. Task 1 is about testing forecasting models for wind speed and power. After a thorough investigation into currently available forecasting methods, several representative models including autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) are developed for short-term wind forecasting. The forecasting performances are evaluated and compared in terms of mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The results reveal that no single model can outperform others universally. This indicates the need of generating a single robust and reliable forecast by applying a post-processing method. As such, a reliable and adaptive model for short-term forecasting the wind power is developed via adaptive Bayesian model averaging algorithms in Task 2. Experiments are performed for both long-term wind assessment and short-term wind forecasting. The results show that the proposed BMA-based model can always provide adaptive, reliable, and iv comparatively accurate forecast results in terms of MAE, RMSE, and MAPE. It also provides a unified approach to tackle the challenging model selection issue in wind forecasting applications. Task 3 is about developing a modeling method for optimizing the wind power bidding process in the deregulated electricity wholesale market. The optimal bids on wind power must take into account the uncertainty in wind forecasts and wind power generation. This research investigates the application of combining improved wind forecasts with agent-based models to optimize the bid and maximize the net earnings. The WSCC 9-bus 3-machine power system network and the IEEE 30-bus 9-GenCo power system network are adopted. Both single-sided and double-sided auctions are considered. The results demonstrate that improving wind forecasting accuracy helps increase the net earnings of wind generation companies, and that the implementation of agent learning algorithms further improves the earnings. The results also verify that agent-based simulation is a viable modeling tool for providing realistic insights about the complex interactions among different market participants and various market factors.
Integrated Projection and Regression Models for Monitoring Multivariate Autocorrelated Cascade Processes
(North Dakota State University, 2014) Khan, Anakaorn
This dissertation presents a comprehensive methodology of dual monitoring for the multivariate autocorrelated cascade processes using principal component analysis and regression. Principle Components Analysis is used to alleviate the multicollinearity among input process variables and reduce the dimension of the variables. An integrated principal components selection rule is proposed to reduce the number of input variables. An autoregressive time series model is used and imposed on the time correlated output variable which depends on many multicorrelated process input variables. A generalized least squares principal component regression is used to describe the relationship between product and process variables under the autoregressive regression error model. The combined residual based EWMA control chart, applied to the product characteristics, and the MEWMA control charts applied to the multivariate autocorrelated cascade process characteristics, are proposed. The dual EWMA and MEWMA control chart has advantage and capability over the conventional residual type control chart applied to the residuals of the principal component regression by monitoring both product and the process characteristics simultaneously. The EWMA control chart is used to increase the detection performance, especially in the case of small mean shifts. The MEWMA is applied to the selected set of variables from the first principal component with the aim of increasing the sensitivity in detecting process failures. The dual implementation control chart for product and process characteristics enhances both the detection and the prediction performance of the monitoring system of the multivariate autocorrelated cascade processes. The proposed methodology is demonstrated through an example of the sugar-beet pulp drying process. A general guideline for controlling multivariate autocorrelated processes is also developed.

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