NDSU Repository

Search Results

Now showing 1 - 10 of 23

Stochastic Optimization of Sustainable Industrial Symbiosis Based Hybrid Generation Bioethanol Supply Chains
(North Dakota State University, 2013) Gonela, Vinay
Bioethanol is becoming increasingly attractive for the reasons of energy security, diversity, and sustainability. As a result, the use of bioethanol for transportation purposes has been encouraged extensively. However, designing an effective bioethanol supply chain that is both sustainable and robust is still questionable. Therefore, this research focuses on designing a bioethanol supply chain that is: 1) sustainable in improving economic, environmental, social, and energy efficiency aspects; and 2) robust to uncertainties such as bioethanol price, bioethanol demand and biomass yield. In this research, we first propose a decision framework to design an optimal bioenergy-based industrial symbiosis (BBIS) under certain constraints. In BBIS, traditionally separate plants collocate in order to efficiently utilize resources, reduce wastes and increase profits for the entire BBIS and each player in the BBIS. The decision framework combines linear programming models and large scale mixed integer linear programming model to determine: 1) best possible combination of plants to form the BBIS, and 2) the optimal multi-product network of various materials in the BBIS, such that the bioethanol production cost is reduced. Secondly, a sustainable hybrid generation bioethanol supply chain (HGBSC), which consists of 1st generation and 2nd generation bioethanol production, is designed to improve economic benefits under environmental and social restrictions. In this study, an optimal HGBSC is designed where the new 2nd generation bioethanol supply chain is integrated with the existing 1st generation bioethanol supply chain under uncertainties such as bioethanol price, bioethanol demand and biomass yield. A stochastic mixed integer linear programming (SMILP) model is developed to design the optimal configuration of HGBSC under different sustainability standards. Finally, a sustainable industrial symbiosis based hybrid generation bioethanol supply chain (ISHGBSC) is designed that incorporates various industrial symbiosis (IS) configurations into HGBSC to improve economic, environmental, social, and energy efficiency aspects of sustainability under bioethanol price, bioethanol demand and biomass yield uncertainties. A SMILP model is proposed to design the optimal ISHGBSC and Sampling Average Approximation algorithm is used as the solution technology. Case studies of North Dakota are used as an application. The results provide managerial insights about the benefits of BBIS configurations within HGBSC.
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.
A Study on Deep Learning for Prognostics and Health Management Applications: An Evolutionary Convolutional Long Short-Term Memory Deep Neural Network Data-Driven Model for Prognostics of Aircraft Gas Turbine
(North Dakota State University, 2022) Khumprom, Phattara
The fundamental concept of prognostics and health management (PHM) within the scope of Condition-Based Maintenance (CBM) is to find an approach to evaluate the system health and predict its remaining useful life (RUL). Many methods and algorithms have been proposed for PHM modeling, most of which have been proven to perform relatively well. One of the leading algorithms in the current data-driven technology era is a deep learning approach, which is based on the concept of multiple hidden layers in a neural network. RUL prediction is an important part of PHM, which is the science that is aimed at increasing the reliability of the system and, in turn, reducing the maintenance cost and potential failure. The majority of the PHM models proposed during the past few years have shown a significant increase in the number systems that are data-driven. While more complex data-driven models are often associated with higher accuracy, there is a corresponding need to reduce model complexity. One possible approach is to reduce the complexity of the model is to use the features (attributes or variables) selection and dimensionality reduction methods before the model training process. In this work, the effectiveness of multiple search-based methods that seek for the best features set to perform model training, which included, filter and wrapper feature selection methods (correlation analysis, relief forward/backward selection, and others), along with Principal Component Analysis (PCA) as a dimensionality reduction method, was investigated. A basic algorithm of deep learning, Feedforward Artificial Neural Network (FFNN), was used as a benchmark modeling algorithm. It is believed that all of those approaches can also be applied to the prognostics of an aircraft engine. The aircraft engine data from NASA Ames prognostics data repository was used to test the effectiveness of the filter and wrapper feature selection methods. The findings show that applying feature selection methods helps to improve overall model accuracy by 3% to 5% compared to other existing works and significantly reduces the complexity by using 7 out of 21 less input nodes for the deep learning type of models.
Stochastic Scheduling Optimization for Solving Patient No-show and Appointment Cancellation Problems
(North Dakota State University, 2015) Peng, Yidong
Patient non-attendance is the major challenge that reduces practice efficiency, resource utilization, and clinic accessibility, and leads to increased cost and diminished quality of care, while the clinic scheduling system is known as a determining factor for clinic efficiency, resource utilization and the accessibility of patients to healthcare facilities. A suitable and optimized scheduling system is one of the most important components for efficient care delivery to address the major challenges in the healthcare industry. Hence, reducing the adverse effect of patient no-shows and short-notice appointment notifications through the appointment scheduling approach is a strategically important matter for any healthcare systems. In this research, three patient scheduling models are proposed to address the patient non-attendance problem in the outpatient clinics. The first model is a two-stage mixed stochastic programming model, which can be used to optimize the overbooking decisions: (1) How many appointment slots should be overbooking; (2) Which appointment slot should be overbooking. In addition, this model also considers the cooperation between providers and patients’ choice. The second model is a Markov Decision Process (MDP) model, which can be used to optimize the walk-in patient admission policy in clinics with single physician by answering the four vital questions: (1) When the walk-in patient admission decisions should be made; (2) At each decision point, how many walk-in patients should be admitted; (3) Which provider should serve the admitted walk-in patients; (4) When the admitted walk-in patient should be served. By using this MDP model, heuristic optimal walk-in patient admission rules have been found for the single physician systems. For systems with more physicians, a more advanced two-stage mixed stochastic programming model (the third model) is proposed in order to make the optimal real time walk-in patient admission decisions. At last, it worthwhile to mention that novel solution approach has also been developed in order to solve these models in the efficient and effective manner.
Multi-Objective Optimal Phasor Measurement Units Placement in Power Systems
(North Dakota State University, 2014) Khiabani, Vahidhossein
The extensive development of power networks has increased the requirements for robust, reliable and secure monitoring and control techniques based on the concept of Wide Area Measurement System (WAMS). Phasor Measurement Units (PMUs) are key elements in WAMS based operations of power systems. Most existing algorithms consider the problem of optimal PMU placement where the main objective is to ensure observability. They consider cost and observability of buses ignoring the reliability aspect of both WAMS and PMUs. Given the twin and conflicting objectives of cost and reliability, this dissertation aims to model and solve a multi-objective optimization formulation that maintains full system observability with minimum cost while exceeding a pre-specified level of reliability of observability. No unique solution exists for these conflicting objectives, hence the model finds the best tradeoffs. Given that the reliability-based PMU placement model is Non-deterministic Polynomial time hard (NP-hard), the mathematical model can only address small problems. This research accomplishes the following: (a) modeling and solving the multi-objective PMU placement model for IEEE standard test systems and its observability, and (b) developing heuristic algorithms to increase the scalability of the model and solve large problems. In short, early consideration of the reliability of observability in the PMU placement problem provides a balanced approach which increases the reliability of the power system overall and reduces the cost of reliability. The findings are helpful to show and understand the effectiveness of the proposed models. However the increased cost associated with the increased reliability would be negligible when considering cost of blackouts to commerce, industry, and society as a whole.
Study of Organizational Transformation from Socio-Technical Perspective
(North Dakota State University, 2020) Rahaman, Md Mahabubur
Organizations are constantly striving for effective and flexible means for managing challenges due to globalization and increasing customer expectations. Many in business community attempted to implement the Toyota Production System, or lean in their organizations to address the challenges. While the intent in many cases were to create a more flexible, effective and efficient organizations that meets the challenges of survival under external and internal pressures. However, the existing body of knowledge on lean is disperse and diverse in nature with respect to the application and implementation of lean tools and practices, making it difficult for researchers and practitioners to gain a real grasp of this topic. This research not only organizes the existing work on implementing lean but also documents challenges of implementation. The primary goal of this research is to study the organizational change and lean transformation from socio-technical perspective. In the process of discovery and empirical research, this work first, identifies challenges of organizational lean transformation. Second, it discovered organizational constructs from socio-technical perspective that has relevance on organizational challenge and lean transformation. Third, it proposed a hypothetical model, create a measurement model for predicting organizational change and lean transformation. Finally, this research tested a set of hypotheses. An Exploratory factor analysis (EFA) and subsequently a confirmatory analysis (CFA) was performed to identify the significance of latent organizational factors from socio-technical perspective as well as provide a theoretical model based on model fit indices exploiting path analysis (PA). This research contributed in providing a meaningful framework for organizational change and lean transformation and develop an instrument for measuring the organizational change and lean transformation for analyzing the gap or identify challenges in lean implementation from socio-technical perspective at organizational levels.
Modeling and Optimization of Biofuel Supply Chain Considering Uncertainties, Hedging Strategies, and Sustainability Concepts
(North Dakota State University, 2013) Awudu, Iddrisu Kaasowa
Due to energy crisis and environmental concerns, alternative energy has attracted a lot of attention in both industry and academia. Biofuel is one type of renewable energy that can reduce the reliance on fossil fuel, and also help reduce environmental effect and provide social benefits. However, to deliver a competitive biofuel product requires a robust supply chain. The biofuel supply chain (BSC) consists of raw material sourcing, transporting of raw materials to pre-treatment and biorefinery sites, pre-treating the raw material, biofuel production, and transporting of the produced biofuel to the final demand zones. As uncertainties are involved throughout the supply chain, risks are introduced. We first propose a stochastic production planning model for a biofuel supply chain under demand and price uncertainties. A stochastic linear programming model is proposed and Benders decomposition (BD) with Monte Carlo simulation technique is applied to solve the proposed model. A case study compares the performance of a deterministic model and the proposed stochastic model. The results indicate that the proposed model obtain higher expected profit than the deterministic model under different uncertainty settings. Sensitivity analyses are performed to gain management insights. Secondly, a hedging strategy is proposed in a hybrid generation biofuel supply chain (HGBSC). A hedging strategy can purchase corn either through futures or spot, while the ethanol end-product sale is hedged using futures. A two-stage stochastic linear programming method with hedging strategy is proposed, and a Multi-cut Benders Decomposition Algorithm is used to solve the proposed model. Prices of feedstock and ethanol end-products are modeled as a mean reversion (MR). The results for both hedging and non-hedging are compared for profit realizations, and the hedging is better as compared to non-hedging for smaller profits. Further sensitivity analyses are conducted to provide managerial insights. Finally, sustainability concepts, which include economic, environmental, and social sustainability, are incorporated in the HGBSC. A two-stage stochastic mixed integer linear programming approach is used, and the proposed HGBSC model is solved using the Lagrangean Relaxation (LR) and Sample Average Approximation (SAA). A representative case study in North Dakota is used for this study.
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.
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.
Optimization of Regional Empty Container Supply Chains to Support Future Investment Decisions for Developing Inland Container Terminals
(North Dakota State University, 2020) Wadhwa, Satpal Singh
Containerized grain shipping has been increasingly used as a shipment option by U.S. exporters. Continued evolution and investment decisions in optimizing multimodal operations is a key in continued growth for the container transportation alternative. Agriculture is a leading sector in the Midwest economy. Grain production is particularly important to the natural resource-based economy of the upper Midwest. These increasing volumes of grain are being shipped in containers because containers offer opportunities to lower logistics costs and to broaden marketing options. Exporters are put at a competitive disadvantage when they are unable to obtain containers at a reasonable cost. Consequently exporters incur large costs to acquire these empty containers which are repositioned empty, from ports and intermodal hubs. When the import and export customers are located inland, empty repositioning generates excessive unproductive empty miles. To mitigate this shortage of empty containers and avoid excessive empty vehicle miles, this research proposes to strategically establish inland depots in regions with sufficiently high agriculture trade volumes. Mathematical models are formulated to evaluate the proposed system to determine the optimal number and location of inland depots in region under varying demand conditions. An agent-based model simulates the complex regional empty container supply chain based on rational individual decisions. The model provides insight into the role of establishing new depot facilities, have on reducing the empty repositioning miles while increasing the grain exports in the region. Model parameters are used to simulate the impact of train frequency and velocity, truck and rail drayage, demand changes at elevators and depot capacity. For the proposed system, stakeholders will be able to quantify the economic impacts of discrete factors like adjustments of the rail and truck rates and impacts of elevator storage capacity. The initial model is limited to a single state (MN) and export market. It could be enhanced to present a flexible logistical scenario assessment tool which is of great help to make investment decisions for improving the efficiency of multimodal transportation. The model can be applied similarly to other commodities and/or be used to analyze the potential for new intermodal points.

Filters

Settings

Sort By

Results per page

Search Results