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Title: Propagation of Unit Location Uncertainty in Dense Storage Environments.
Creator: Reilly, Patrick, Pazour, Jennifer, Zheng, Qipeng, Schneider, Kellie, University of Central Florida
Abstract / Description: Effective space utilization is an important consideration in logistics systems and is especially important in dense storage environments. Dense storage systems provide high-space utilization; however, because not all items are immediately accessible, storage and retrieval operations often require shifting of other stored items in order to access the desired item, which results in item location uncertainty when asset tracking is insufficient. Given an initial certainty in item location, we use...
Show moreEffective space utilization is an important consideration in logistics systems and is especially important in dense storage environments. Dense storage systems provide high-space utilization; however, because not all items are immediately accessible, storage and retrieval operations often require shifting of other stored items in order to access the desired item, which results in item location uncertainty when asset tracking is insufficient. Given an initial certainty in item location, we use Markovian principles to quantify the growth of uncertainty as a function of retrieval requests and discover that the steady state probability distribution for any communicating class of storage locations approaches uniform. Using this result, an expected search time model is developed and applied to the systems analyzed. We also develop metrics that quantify and characterize uncertainty in item location to aid in understanding the nature of that uncertainty. By incorporating uncertainty into our logistics model and conducting numerical experiments, we gain valuable insights into the uncertainty problem such as the benefit of multiple item copies in reducing expected search time and the varied response to different retrieval policies in otherwise identical systems.
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Date Issued: 2015
Identifier: CFE0006052, ucf:50972
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006052

Title: Shop Scheduling in the Presence of Batching, Sequence-Dependent Setups and Incompatible Job Families Minimizing Earliness and Tardiness Penalties.
Creator: Buchanan, Patricia, Geiger, Christopher, Mollaghasemi, Mansooreh, Pazour, Jennifer, Nazzal, Dima, University of Central Florida
Abstract / Description: The motivation of this research investigation stems from a particular job shop production environment at a large international communications and information technology company in which electro-mechanical assemblies (EMAs) are produced. The production environment of the EMAs includes the continuous arrivals of the EMAs (generally called jobs), with distinct due dates, degrees of importance and routing sequences through the production workstations, to the job shop. Jobs are processed in...
Show moreThe motivation of this research investigation stems from a particular job shop production environment at a large international communications and information technology company in which electro-mechanical assemblies (EMAs) are produced. The production environment of the EMAs includes the continuous arrivals of the EMAs (generally called jobs), with distinct due dates, degrees of importance and routing sequences through the production workstations, to the job shop. Jobs are processed in batches at the workstations, and there are incompatible families of jobs, where jobs from different product families cannot be processed together in the same batch. In addition, there are sequence-dependent setups between batches at the workstations. Most importantly, it is imperative that all product deliveries arrive on time to their customers (internal and external) within their respective delivery time windows. Delivery is allowed outside a time window, but at the expense of a penalty. Completing a job and delivering the job before the start of its respective time window results in a penalty, i.e., inventory holding cost. Delivering a job after its respective time window also results in a penalty, i.e., delay cost or emergency shipping cost. This presents a unique scheduling problem where an earliness-tardiness composite objective is considered.This research approaches this scheduling problem by decomposing this complex job shop scheduling environment into bottleneck and non-bottleneck resources, with the primary focus on effectively scheduling the bottleneck resource. Specifically, the problem of scheduling jobs with unique due dates on a single workstation under the conditions of batching, sequence-dependent setups, incompatible job families in order to minimize weighted earliness and tardiness is formulated as an integer linear program. This scheduling problem, even in its simplest form, is NP-Hard, where no polynomial-time algorithm exists to solve this problem to optimality, especially as the number of jobs increases. As a result, the computational time to arrive at optimal solutions is not of practical use in industrial settings, where production scheduling decisions need to be made quickly. Therefore, this research explores and proposes new heuristic algorithms to solve this unique scheduling problem. The heuristics use order review and release strategies in combination with priority dispatching rules, which is a popular and more commonly-used class of scheduling algorithms in real-world industrial settings. A computational study is conducted to assess the quality of the solutions generated by the proposed heuristics. The computational results show that, in general, the proposed heuristics produce solutions that are competitive to the optimal solutions, yet in a fraction of the time. The results also show that the proposed heuristics are superior in quality to a set of benchmark algorithms within this same class of heuristics.
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Date Issued: 2014
Identifier: CFE0005139, ucf:50717
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005139

Title: A Holistic Framework for Transitional Management.
Creator: Elattar, Ahmed, Rabelo, Luis, Pazour, Jennifer, Mollaghasemi, Mansooreh, Ajayi, Richard, University of Central Florida
Abstract / Description: For all business organizations, there comes a time when a change must take place within their eco-system. It consumes a great deal of thought and planning to ensure that the right decision is made as it could alter the entire course of their business for a number of years to come. This change may appear in the form of a brilliant CEO reaching the age of retirement, or an unsuccessful Managing Director being asked to leave before fulfilling the term of her contract. Regardless of the cause, a...
Show moreFor all business organizations, there comes a time when a change must take place within their eco-system. It consumes a great deal of thought and planning to ensure that the right decision is made as it could alter the entire course of their business for a number of years to come. This change may appear in the form of a brilliant CEO reaching the age of retirement, or an unsuccessful Managing Director being asked to leave before fulfilling the term of her contract. Regardless of the cause, a transition must occur in which a suitable successor is chosen and put into place while minimizing costs, satisfying stakeholders, ensuring that the successor has been adequately prepared for their new position, and minimizing work place gossip, among other things. It is also important to understand how the nature of the business, as well as its financial standing, effects such a transition.Engineering and management principles come together in this study to ensure that organizations going through such a change are on the right course. As the problem of transitional management is not one of concrete values and contains many ambiguous concepts, one way to tackle the problem is by utilizing various industrial engineering methodologies that allow these companies to systematically begin preparing for such a change. By default, organizational strategy has to change, technology is continually being renewed and it becomes very hard for the same leader to constantly implement new and innovative developments.Organizations today have a very poor understanding of where they currently stand and as a result the cause for a company's lack of profitability is often overlooked with time and money being wasted in an attempt to fix something that is not broken. To be able to look at the bigger picture of an organization and from there begin to close in on the main problems causing a negative impact, the Matrix of Change is used and takes in many factors to layout an accurate representation of the direction in which an organization should be headed and how it can continue to grow and remain successful. The Theory of Constraints on the other hand is used here as a step-by-step guide allowing companies to be better organized during times of change. And System Dynamics modeling is where these companies can begin to simulate and solve the dilemma of transitional management using causal loop diagrams and stock and flow diagrams.Through such tools a framework can begin to be developed, one that is valued by corporations and continually reviewed. Several case studies, simulation modeling, and a panel of experts were used in order to demonstrate and validate this framework.
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Date Issued: 2014
Identifier: CFE0005160, ucf:50708
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005160

Title: An Unsupervised Consensus Control Chart Pattern Recognition Framework.
Creator: Haghtalab, Siavash, Xanthopoulos, Petros, Pazour, Jennifer, Rabelo, Luis, University of Central Florida
Abstract / Description: Early identification and detection of abnormal time series patterns is vital for a number of manufacturing.Slide shifts and alterations of time series patterns might be indicative of some anomalyin the production process, such as machinery malfunction. Usually due to the continuous flow of data monitoring of manufacturing processes requires automated Control Chart Pattern Recognition(CCPR) algorithms. The majority of CCPR literature consists of supervised classification algorithms. Less...
Show moreEarly identification and detection of abnormal time series patterns is vital for a number of manufacturing.Slide shifts and alterations of time series patterns might be indicative of some anomalyin the production process, such as machinery malfunction. Usually due to the continuous flow of data monitoring of manufacturing processes requires automated Control Chart Pattern Recognition(CCPR) algorithms. The majority of CCPR literature consists of supervised classification algorithms. Less studies consider unsupervised versions of the problem. Despite the profound advantageof unsupervised methodology for less manual data labeling their use is limited due to thefact that their performance is not robust enough for practical purposes. In this study we propose the use of a consensus clustering framework. Computational results show robust behavior compared to individual clustering algorithms.
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Date Issued: 2014
Identifier: CFE0005178, ucf:50670
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005178

Title: Resource allocation and load-shedding policies based on Markov decision processes for renewable energy generation and storage.
Creator: Jimenez, Edwards, Atia, George, Richie, Samuel, Pazour, Jennifer, University of Central Florida
Abstract / Description: In modern power systems, renewable energy has become an increasingly popular form of energy generation as a result of all the rules and regulations that are being implemented towards achieving clean energy worldwide. However, clean energy can have drawbacks in several forms. Wind energy, for example can introduce intermittency. In this thesis, we discuss a method to deal with this intermittency. In particular, by shedding some specific amount of load we can avoid a total system breakdown of...
Show moreIn modern power systems, renewable energy has become an increasingly popular form of energy generation as a result of all the rules and regulations that are being implemented towards achieving clean energy worldwide. However, clean energy can have drawbacks in several forms. Wind energy, for example can introduce intermittency. In this thesis, we discuss a method to deal with this intermittency. In particular, by shedding some specific amount of load we can avoid a total system breakdown of the entire power plant. The load shedding method discussed in this thesis utilizes a Markov Decision Process with backward policy iteration. This is based on a probabilistic method that chooses the best load-shedding path that minimizes the expected total cost to ensure no power failure. We compare our results with two control policies, a load-balancing policy and a less-load shedding policy. It is shown that the proposed MDP policy outperforms the other control policies and achieves the minimum total expected cost.
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Date Issued: 2015
Identifier: CFE0005635, ucf:50222
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005635

Title: Inventory Management Problem for Cold Items with Environmental and Financial Considerations.
Creator: Hajiaghabozorgi, Ali, Pazour, Jennifer, Karwowski, Waldemar, Zheng, Qipeng, Nazzal, Dima, University of Central Florida
Abstract / Description: The overarching theme of this dissertation is analytically analyzing the cold supply chain from a financial and environmental perspective. Specifically, we develop inventory policy models in the cold supply chain that consider holding and transportation unit capacities. The models provide insights for the decision maker on the tradeoff between setting order quantities based on the cost or the emission function.In Chapter 2, we review two major bodies of literature: 1) supply chain design, and...
Show moreThe overarching theme of this dissertation is analytically analyzing the cold supply chain from a financial and environmental perspective. Specifically, we develop inventory policy models in the cold supply chain that consider holding and transportation unit capacities. The models provide insights for the decision maker on the tradeoff between setting order quantities based on the cost or the emission function.In Chapter 2, we review two major bodies of literature: 1) supply chain design, and 2) sustainability in supply chain design. We benefit from this literature review to map the current body of research on traditional supply chain for further comparison with the cold supply chain. Sustainability in supply chain network design is often measured by the carbon footprint; other sustainability metrics such as water footprint and sustainable energy are not included. Literature on supply chain design can be further broken down into its three major components: 1) facility location/allocation, 2) inventory management, and 3) facility location/allocation combined with inventory management. In Chapter 3, we study and present an overview of the cold chain. In accordance to the three levels of supply chain management decision making, the study is divided into the following three sections: (1) strategic level, (2) tactical level, and (3) operational level. Specifically, we capture how these decisions will impact the three main components of sustainability: economic, environmental, and social components. In addition, we explain how these components are different in the cold chain, in comparison to the traditional supply chain, and why such unique differences are worth studying. The intent of this chapter is to provide an overview of cold chains and to identify open areas for research. Examples from industrial cases, in addition to data and information from white papers, reports and research articles are provided.In Chapter 4, the cold item inventory problem is formulated as a single-period model that considers both financial and emissions functions. A new formulation for holding and transportation cost and emission is proposed by considering unit capacity for holding and transportation. This model applies to cold items that need to be stored at a certain, non-ambient temperature. Holding cold items in a warehouse is usually done by dividing the warehouse into a set of cold freezer units inside rather than refrigerating the entire warehouse. The advantage of such a design is that individual freezer units can be turned off to save cost and energy, when they are not needed. As a result, there is a fixed (setup) cost for holding a group of items, which results in a step function to represent the fixed cost of turning on the freezer units, in addition to the variable cost of holding items based on the number of units held in inventory. Three main goals of studying this problem are: 1) deriving the mathematical structure and modeling the holding and transportation costs and environmental functions in cold chains, 2) proposing exact solution procedures to solve the math models, and 3) analyzing the tradeoffs involved in making inventory decisions based on minimizing emissions vs. minimizing cost in cold chains.This problem demonstrates the tradeoff between the cost and the emission functions in an important supply chain decision. Also, the analytical models and solution approaches provide the decision maker with analytical tools for making better decisions.In Chapter 5, we expand the developed model from Chapter 4 to include multiple types of products. We consider a group of products that share capacities as a family of products. According to the problem formulation, we have two types of decision variables: (1) determining if a product is a member of a family or not, and (2) how much to order and how frequently to order for products within each family. We propose a solution procedure in accordance with the decision variable types: (1) a procedure for grouping (partitioning) the products into different families, and (2) a procedure to solve the inventory problem for each family. A set of experiments are designed to answer a number of research questions, and brings more understandings of the developed models and solutions algorithms.Finally, the conclusions of this dissertation and suggestions for future research topics are presented in Chapter 6.
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Date Issued: 2014
Identifier: CFE0005501, ucf:50365
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005501

Title: Cost-Sensitive Learning-based Methods for Imbalanced Classification Problems with Applications.
Creator: Razzaghi, Talayeh, Xanthopoulos, Petros, Karwowski, Waldemar, Pazour, Jennifer, Mikusinski, Piotr, University of Central Florida
Abstract / Description: Analysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties createbias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive...
Show moreAnalysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties createbias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive learning method (CSL) to deal with the classification of imperfect data. Typically, most traditional approaches for classification demonstrate poor performance in an environment with imperfect data. We propose the use of CSL with Support Vector Machine, which is a well-known data mining algorithm. The results reveal that the proposed algorithm produces more accurate classifiers and is more robust with respect to imperfect data. Furthermore, we explore the best performance measures to tackle imperfect data along with addressing real problems in quality control and business analytics.
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Date Issued: 2014
Identifier: CFE0005542, ucf:50298
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005542

Title: Modeling and Analysis of Automated Storage and Retrievals System with Multiple in-the-aisle Pick Positions.
Creator: Ramtin, Faraz, Pazour, Jennifer, Reilly, Charles, Xanthopoulos, Petros, Goodman, Stephen, University of Central Florida
Abstract / Description: This dissertation focuses on developing analytical models for automated storage and retrieval system with multiple in-the-aisle pick positions (MIAPP-AS/RS). Specifically, our first contribution develops an expected travel time model for different pick positions and different physical configurations for a random storage policy. This contribution has been accepted for publication in IIE Transactions (Ramtin (&) Pazour, 2014) and was the featured article in the IE Magazine (Askin (&) Nussbaum,...
Show moreThis dissertation focuses on developing analytical models for automated storage and retrieval system with multiple in-the-aisle pick positions (MIAPP-AS/RS). Specifically, our first contribution develops an expected travel time model for different pick positions and different physical configurations for a random storage policy. This contribution has been accepted for publication in IIE Transactions (Ramtin (&) Pazour, 2014) and was the featured article in the IE Magazine (Askin (&) Nussbaum, 2014). The second contribution addresses an important design question associated with MIAPP-AS/RS, which is the assignment of items to pick positions in an MIAPP-AS/RS. This contribution has been accepted for publication in IIE Transactions (Ramtin (&) Pazour, 2015). Finally, the third contribution is to develop travel time models and to determine the optimal SKUs to storage locations assignment under different storage assignment polies such as dedicated and class-based storage policies for MIAPP-AS/RS.An MIAPP-AS/RS is a case-level order-fulfillment technology that enables order picking via multiple pick positions (outputs) located in the aisle. We develop expected travel time models for different operating policies and different physical configurations. These models can be used to analyze MIAPP-AS/RS throughput performance during peak and non-peak hours. Moreover, closed-form approximations are derived for the case of an infinite number of pick positions, which enable us to derive the optimal shape configuration that minimizes expected travel times. We compare our expected travel time models with a simulation model of a discrete rack, and the results validate that our models provide good estimates. Finally, we conduct a numerical experiment to illustrate the trade-offs between performance of operating policies and design configurations. We find that MIAPP-AS/RS with a dual picking floor and input point is a robust configuration because a single command operating policy has comparable throughput performance to a dual command operating policy.As a second contribution, we study the impact of selecting different pick position assignments on system throughput, as well as system design trade-offs that occur when MIAPP-AS/RS is running under different operating policies and different demand profiles. We study the impact of product to pick position assignments on the expected throughput for different operating policies, demand profiles, and shape factors. We develop efficient algorithms of complexity O(nlog(n)) that provide the assignment that minimizes the expected travel time. Also, for different operating policies, shape configurations, and demand curves, we explore the structure of the optimal assignment of products to pick positions and quantify the difference between using a simple, practical assignment policy versus the optimal assignment. Finally, we derive closed-form analytical travel time models by approximating the optimal assignment's expected travel time using continuous demand curves and assuming an infinite number of pick positions in the aisle. We illustrate that these continuous models work well in estimating the travel time of a discrete rack and use them to find optimal design configurations.As the third and final contribution, we study the impact of dedicated and class-based storage policy on the performance of MIAPP-AS/RS. We develop mathematical optimization models to minimize the travel time of the crane by changing the assignment of the SKUs to pick positions and storage locations simultaneously. We develop a more tractable solution approach by applying a Benders decomposition approach, as well as an accelerated procedure for the Benders algorithm. We observe high degeneracy for the optimal solution when we use chebyshev metric to calculate the distances. As the result of this degeneracy, we realize that the assignment of SKUs to pick positions does not impact the optimal solution. We also develop closed-form travel time models for MIAPP-AS/RS under a class-based storage policy.
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Date Issued: 2015
Identifier: CFE0005695, ucf:50142
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005695

Title: Integrated Sustainability Assessment Framework for the U.S. Transportation.
Creator: Onat, Nuri, Tatari, Omer, Nam, Boo Hyun, Oloufa, Amr, Pazour, Jennifer, University of Central Florida
Abstract / Description: This dissertation aims to investigate the sustainability impacts of alternative vehicle technologies and develop comprehensive sustainability assessment frameworks to analyze potential impacts of these vehicles in the U.S. In order to assess sustainability impact of vehicle alternatives, life-cycle based models has been extensively used in the literature. Although life cycle-based models are often used for environmental impacts of alternative vehicles, analysis of social and economic impacts...
Show moreThis dissertation aims to investigate the sustainability impacts of alternative vehicle technologies and develop comprehensive sustainability assessment frameworks to analyze potential impacts of these vehicles in the U.S. In order to assess sustainability impact of vehicle alternatives, life-cycle based models has been extensively used in the literature. Although life cycle-based models are often used for environmental impacts of alternative vehicles, analysis of social and economic impacts of these vehicles has gained a tremendous interest. In this regard, there is a growing interest among the international platform and academia to use the Life Cycle Sustainability Assessment framework to have more informed sustainable products, material and technology choices by considering the environmental, as well as social and economic impacts. The Life Cycle Sustainability Assessment framework is still under development and there is an ongoing research to advance it for future applications. In this dissertation, current and future needs of sustainability assessment frameworks and the U.S. transportation are identified and addressed. The major research gaps are identified as follows: (1) there has been small emphasis on effects of spatial and temporal variations on the sustainability impacts of alternative vehicle technologies, (2) no national research efforts as of now have been directed specifically toward understanding the fundamental relationship between the adoption of electric vehicles and water demand, (3) there has been a lack of understanding the dynamic complexity of transportation sustainability, encompassing feedback mechanisms, and interdependencies, for the environmental, social, and economic impacts of alternative vehicles, and (4) there is no emphasis on addressing uncertainties inherent to the U.S. transportation and its complex relationships with the environment, society, and economy.The environmental, economic, and social impacts of alternative vehicles are highly critical for truly assessing and understanding the long-term sustainability of vehicles and propose economically viable, socially acceptable, and environmentally-friendly transportation solutions for U.S. passenger transportation. This dissertation provides a more comprehensive sustainability assessment framework by realizing following objectives: (1) inclusion of spatial and temporal variations when quantifying carbon, energy, and water footprints of alternative vehicle technologies, (2) quantifying environmental, social, and economic impacts of alternative vehicle technologies, (3) capturing the dynamic relations among the parameters of U.S. transportation system, environment, society, and the economy, (4) dealing with uncertainties inherent to the U.S. transportation sector considering the complexity of the system and dynamic relationships. The results of this dissertation reveal that the results with consideration of uncertainties, temporal and spatial variations, and dynamic complex relationships among the system variables can be significantly different than those of without consideration of those. Therefore, when developing policies the robustness of proposed scenarios should be valuated with consideration of uncertainties, temporal and spatial variations as well as the dynamic feedback mechanisms. The outcomes of this study can pave the way for advancement in the state-of-the-art and state-of-the-practice in the sustainability research by presenting novel approaches to deal with uncertainties and complex systems.
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Date Issued: 2015
Identifier: CFE0005857, ucf:50904
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005857

Title: A Macro-Level Sustainability Assessment Framework for Optimal Distribution of Alternative Passenger Vehicles.
Creator: Onat, Nuri, Tatari, Omer, Nam, Boo Hyun, Oloufa, Amr, Pazour, Jennifer, University of Central Florida
Abstract / Description: Although there are many studies focusing on the environmental impacts of alternative vehicle options, social and economic dimensions and trade-off relationships among all of these impacts were not investigated sufficiently. Moreover, most economic analyses are limited to life cycle cost analyses and do not consider macro-level economic impacts. Therefore, this thesis aims to advance the Life Cycle Sustainability Assessment literature and electric vehicle sustainability research by presenting...
Show moreAlthough there are many studies focusing on the environmental impacts of alternative vehicle options, social and economic dimensions and trade-off relationships among all of these impacts were not investigated sufficiently. Moreover, most economic analyses are limited to life cycle cost analyses and do not consider macro-level economic impacts. Therefore, this thesis aims to advance the Life Cycle Sustainability Assessment literature and electric vehicle sustainability research by presenting a novel combined application of Multi Criteria Decision Making techniques with Life Cycle Sustainability Assessment for decision analysis. With this motivation in mind, this research will construct a compromise-programming model (multi-objective optimization method) in order to calculate the optimum vehicle distribution in the U.S. passenger car fleet while considering the trade-offs between environmental, economic, and social dimensions of the sustainability. The findings of this research provide important insights for policy makers when developing strategies to estimate optimum vehicle distribution strategies based on various environmental and socio-economic priorities. For instance, compromise programming results can present practical policy conclusions for different states which might have different priorities for environmental impact mitigation and socio-economic development. Therefore, the conceptual framework presented in this work can be applicable for different regions in U.S. and decision makers can generate balanced policy conclusions and recommendations based on their environmental, economic and social constraints. The compromise programming results provide vital guidance for policy makers when optimizing the use of alternative vehicle technologies based on different environmental and socio-economic priorities. This research also effort aims to increase awareness of the inherent benefits of Input-Output based a Life Cycle Sustainability Assessment and multi-criteria optimization.
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Date Issued: 2015
Identifier: CFE0005858, ucf:50901
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005858

Title: Modeling and Solving Large-scale Stochastic Mixed-Integer Problems in Transportation and Power Systems.
Creator: Huang, Zhouchun, Zheng, Qipeng, Xanthopoulos, Petros, Pazour, Jennifer, Chang, Ni-bin, University of Central Florida
Abstract / Description: In this dissertation, various optimization problems from the area of transportation and power systems will be respectively investigated and the uncertainty will be considered in each problem. Specifically, a long-term problem of electricity infrastructure investment is studied to address the planning for capacity expansion in electrical power systems with the integration of short-term operations. The future investment costs and real-time customer demands cannot be perfectly forecasted and...
Show moreIn this dissertation, various optimization problems from the area of transportation and power systems will be respectively investigated and the uncertainty will be considered in each problem. Specifically, a long-term problem of electricity infrastructure investment is studied to address the planning for capacity expansion in electrical power systems with the integration of short-term operations. The future investment costs and real-time customer demands cannot be perfectly forecasted and thus are considered to be random. Another maintenance scheduling problem is studied for power systems, particularly for natural gas fueled power plants, taking into account gas contracting and the opportunity of purchasing and selling gas in the spot market as well as the maintenance scheduling considering the uncertainty of electricity and gas prices in the spot market. In addition, different vehicle routing problems are researched seeking the route for each vehicle so that the total traveling cost is minimized subject to the constraints and uncertain parameters in corresponding transportation systems.The investigation of each problem in this dissertation mainly consists of two parts, i.e., the formulation of its mathematical model and the development of solution algorithm for solving the model. The stochastic programming is applied as the framework to model each problem and address the uncertainty, while the approach of dealing with the randomness varies in terms of the relationships between the uncertain elements and objective functions or constraints. All the problems will be modeled as stochastic mixed-integer programs, and the huge numbers of involved decision variables and constraints make each problem large-scale and very difficult to manage. In this dissertation, efficient algorithms are developed for these problems in the context of advanced methodologies of optimization and operations research, such as branch and cut, benders decomposition, column generation and Lagrangian method. Computational experiments are implemented for each problem and the results will be present and discussed. The research carried out in this dissertation would be beneficial to both researchers and practitioners seeking to model and solve similar optimization problems in transportation and power systems when uncertainty is involved.
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Date Issued: 2016
Identifier: CFE0006328, ucf:51559
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006328

Title: Modeling Dense Storage Systems With Location Uncertainty.
Creator: Awwad, Mohamed, Pazour, Jennifer, Elshennawy, Ahmad, Thompson, William, Leon, Steven, University of Central Florida
Abstract / Description: This dissertation focuses on developing models to study the problem of searching and retrieving items in a dense storage environment. We consider a special storage configuration called an inverted T configuration, which has one horizontal and one vertical aisle. Inverted T configurations have fewer aisles than a traditional aisle-based storage environment. This increases the storage density; however, requires that some items to be moved out of the way to gain access to other more deeply...
Show moreThis dissertation focuses on developing models to study the problem of searching and retrieving items in a dense storage environment. We consider a special storage configuration called an inverted T configuration, which has one horizontal and one vertical aisle. Inverted T configurations have fewer aisles than a traditional aisle-based storage environment. This increases the storage density; however, requires that some items to be moved out of the way to gain access to other more deeply stored items. Such movement can result in item location uncertainty. When items are requested for retrieval in a dense storage environment with item location uncertainty, searching is required. Dense storage has a practical importance as it allows for the use of available space efficiently, which is especially important with the scarce and expensive space onboard of US Navy's ships that form a sea base. A sea base acts as a floating distribution center that provides ready issue material to forces ashore participating in various types of missions. The sea basing concept and the importance of a sea base's responsiveness is our main motivation to conduct this research.In chapter 2, we review three major bodies of literature: 1) sea based logistics, 2) dense storage and 3) search theory. Sea based logistics literature mostly focuses on the concept and the architecture of a sea base, with few papers developing mathematical models to solve operational problems of a sea base, including papers handling the logistical and sustainment aspects. Literature related to dense storage can be broken down into work dealing with a dense storage environment with an inverted T configuration and other papers dealing with other dense storage configurations. It was found that some of the dense storage literature was motivated by the same application, i.e. sea based logistics. Finally, we surveyed the vast search theory literature and classification of search environments. This research contributes to the intersection of these three bodies of literature. Specifically, this research, motivated by the application of sea basing, develops search heuristics for dense storage environments that require moving items out of the way during searching. In chapter 3, we present the problem statements. We study two single-searcher search problems. The first problem is searching for a single item in an inverted T dense storage environment. The second one is searching for one or more items in an inverted T storage environment with items stacked over each other in the vertical direction.In chapter 4, we present our first contribution. In this contribution we propose a search plan heuristic to search for a single item in an inverted T, k-deep dense storage system with the objective of decreasing the expected search time in such an environment. In this contribution, we define each storage environment entirely by the accessibility constant and the storeroom length. In addition, equations are derived to calculate each component of the search time equation that we propose: travel, put-back and repositioning. Two repositioning policies are studied. We find that a repositioning policy that uses the open aisle locations as temporary storage locations and requires put-back of these items while searching is recommended. This recommendation is because such a policy results in lower expected search time and lower variability than a policy that uses available space outside the storage area and handles put-back independently of the search process. Statistical analysis is used to analyze the numerical results of the first contribution and to analyze the performances of both repositioning polices. We derive the probability distribution of search times in a storeroom with small configurations in terms of the accessibility constant and length. It was found that this distribution can be approximated using a lognormal probability distribution with a certain mean and standard deviation. Knowing the probability distribution provides the decision makers with the full range of all possible probabilities of search times, which is useful for downstream planning operations.In chapter 5, we present the second contribution, in which we propose a search plan heuristic but for multiple items in an inverted T, k-deep storage system. Additionally, we consider stacking multiple items over each other. Stacking items over each other, increases the number of stored items and allows for the utilization of the vertical space. In this second contribution, we are using the repositioning policy that proved its superiority in the first contribution. This contribution investigates a more general and a much more challenging environment than the one studied in the first contribution. In the second environment, to gain access to some items, not only may other items need to be moved out of the way, but also the overall number of movements for items within the system will be highly affected by the number of items stacked over each other. In addition, the searcher is given a task that includes searching and retrieving a set of items, rather than just one item.For the second contribution, the performance of the search heuristic is analyzed through a Statistical Design of Experiments, and it was found that searching and retrieving multiple items instead of just a single item, would decrease the variability in search times for each storeroom configuration. Finally, in chapter 6, conclusions of this research and suggestions for future research directions are presented.
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Date Issued: 2015
Identifier: CFE0006256, ucf:51045
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006256

Title: Optimal distribution network reconfiguration using meta-heuristic algorithms.
Creator: Asrari, Arash, Wu, Thomas, Lotfifard, Saeed, Haralambous, Michael, Atia, George, Pazour, Jennifer, University of Central Florida
Abstract / Description: Finding optimal configuration of power distribution systems topology is an NP-hard combinatorial optimization problem. It becomes more complex when time varying nature of loads in large-scale distribution systems is taken into account. In the second chapter of this dissertation, a systematic approach is proposed to tackle the computational burden of the procedure. To solve the optimization problem, a novel adaptive fuzzy based parallel genetic algorithm (GA) is proposed that employs the...
Show moreFinding optimal configuration of power distribution systems topology is an NP-hard combinatorial optimization problem. It becomes more complex when time varying nature of loads in large-scale distribution systems is taken into account. In the second chapter of this dissertation, a systematic approach is proposed to tackle the computational burden of the procedure. To solve the optimization problem, a novel adaptive fuzzy based parallel genetic algorithm (GA) is proposed that employs the concept of parallel computing in identifying the optimal configuration of the network. The integration of fuzzy logic into GA enhances the efficiency of the parallel GA by adaptively modifying the migration rates between different processors during the optimization process. A computationally efficient graph encoding method based on Dandelion coding strategy is developed which automatically generates radial topologies and prevents the construction of infeasible radial networks during the optimization process. The main shortcoming of the proposed algorithm in Chapter 2 is that it identifies only one single solution. It means that the system operator will not have any option but relying on the found solution. That is why a novel hybrid optimization algorithm is proposed in the third chapter of this dissertation that determines Pareto frontiers, as candidate solutions, for multi-objective distribution network reconfiguration problem. Implementing this model, the system operator will have more flexibility in choosing the best configuration among the alternative solutions. The proposed hybrid optimization algorithm combines the concept of fuzzy Pareto dominance (FPD) with shuffled frog leaping algorithm (SFLA) to recognize non-dominated suboptimal solutions identified by SFLA. The local search step of SFLA is also customized for power systems applications so that it automatically creates and analyzes only the feasible and radial configurations in its optimization procedure which significantly increases the convergence speed of the algorithm. In the fourth chapter, the problem of optimal network reconfiguration is solved for the case in which the system operator is going to employ an optimization algorithm that is automatically modifying its parameters during the optimization process. Defining three fuzzy functions, the probability of crossover and mutation will be adaptively tuned as the algorithm proceeds and the premature convergence will be avoided while the convergence speed of identifying the optimal configuration will not decrease. This modified genetic algorithm is considered a step towards making the parallel GA, presented in the second chapter of this dissertation, more robust in avoiding from getting stuck in local optimums. In the fifth chapter, the concentration will be on finding a potential smart grid solution to more high-quality suboptimal configurations of distribution networks. This chapter is considered an improvement for the third chapter of this dissertation for two reasons: (1) A fuzzy logic is used in the partitioning step of SFLA to improve the proposed optimization algorithm and to yield more accurate classification of frogs. (2) The problem of system reconfiguration is solved considering the presence of distributed generation (DG) units in the network. In order to study the new paradigm of integrating smart grids into power systems, it will be analyzed how the quality of suboptimal solutions can be affected when DG units are continuously added to the distribution network.The heuristic optimization algorithm which is proposed in Chapter 3 and is improved in Chapter 5 is implemented on a smaller case study in Chapter 6 to demonstrate that the identified solution through the optimization process is the same with the optimal solution found by an exhaustive search.
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Date Issued: 2015
Identifier: CFE0005575, ucf:50238
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005575

Title: A Real Option Dynamic Decision (RODD) Framework for Operational Innovations.
Creator: Onkham, Wilawan, Rabelo, Luis, O'Neal, Thomas, Pazour, Jennifer, Yazici, Hulya Julie, University of Central Florida
Abstract / Description: Changing the business operations and adopting new operational innovations, have become key features for a business solution approach. However, there are challenges for developing innovative operations due to a lack of the proper decision analysis tools, lack of understanding the impacts transition will have on operational models, and the time limits of the innovation life cycle. The cases of business failure in operational innovation (i.e. Eastman Kodak Company and Borders Group Inc.,)...
Show moreChanging the business operations and adopting new operational innovations, have become key features for a business solution approach. However, there are challenges for developing innovative operations due to a lack of the proper decision analysis tools, lack of understanding the impacts transition will have on operational models, and the time limits of the innovation life cycle. The cases of business failure in operational innovation (i.e. Eastman Kodak Company and Borders Group Inc.,) support the need for an investment decision framework. This research aims to develop a Real Option Dynamic Decision (RODD) framework for decision making, to support decision makers for operational innovation investments. This development will help the business/organization to recognize the need for change in operations, and quickly respond to market threats and customer needs. The RODD framework is developed by integrating a strategic investment method (Real Options Analysis), management transition evaluation (Matrix of Change), competitiveness evaluation (Lotka-Volterra), and dynamic behavior modeling (System Dynamics Modeling) to analyze the feasibility of the transformation, and to assess return on investment of new operation schemes. Two case studies are used: United Parcel Service of America, Inc., and Firefighting Operations to validate the RODD framework. The results show that the benefits of this decision-making framework are (1) to provide increased flexibility, improved predictions, and more information to decision makers; (2) to assess the value alternative option with regards to uncertainty and competitiveness; (3) to reduce complexity; and (4) to gain a new understanding of operational innovations.
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Date Issued: 2013
Identifier: CFE0005039, ucf:50002
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005039

Title: Managing, Controlling and Improving the Treatment of Produced Water Using the Six Sigma Methodology for the Iraqi Oil Fields.
Creator: Al-Shamkhani, Maher T., Elshennawy, Ahmad, Rabelo, Luis, Pazour, Jennifer, Xanthopoulos, Petros, University of Central Florida
Abstract / Description: Produced Water (PW) is the largest volume of waste that is normally generated during oil and gas production. It has large amounts of contaminants that can cause negative environmental and economic impacts. The management method for PW relies highly on types and concentrations of these contaminants, which are field dependent and can vary from one oil field to another. Produced water can be converted to fresh water if these contaminants are removed or reduced to the acceptable drinking water...
Show moreProduced Water (PW) is the largest volume of waste that is normally generated during oil and gas production. It has large amounts of contaminants that can cause negative environmental and economic impacts. The management method for PW relies highly on types and concentrations of these contaminants, which are field dependent and can vary from one oil field to another. Produced water can be converted to fresh water if these contaminants are removed or reduced to the acceptable drinking water quality level. In addition, increasing oil production rate and reducing amounts of discharged harmful contaminants can be achieved by removing dissolved hydrocarbons from PW. In order to identify the types of these contaminants, effective tools and methods should be used. Six Sigma, which uses the DMAIC (Define- Measure- Analyze- Improve- Control) problem-solving approach is one of the most effective tools to identify the root causes of having high percentages of contaminants in produced water. The methodology also helped develop a new policy change for implementing a way by which this treated water may be used. Six Sigma has not been widely implemented in oil and gas industries. This research adopted the Six Sigma methodology through a case study, related to the southern Iraqi oil fields, to investigate different ways by which produced water can be treated. Research results showed that the enormous amount of contaminated PW could be treated by using membrane filtration technology. In addition, a Multi Criteria Decision Making (MCDM) framework is developed and that could be used as an effective tool for decision makers. The developed framework could be used within manufacturing industries, services, educational systems, governmental organizations, and others.
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Date Issued: 2013
Identifier: CFE0004645, ucf:49904
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004645

Title: Life Cycle Sustainability Assessment Framework for the U.S. Built Environment.
Creator: Kucukvar, Murat, Tatari, Mehmet, Oloufa, Amr, Behzadan, Amir, Al-Deek, Haitham, Pazour, Jennifer, University of Central Florida
Abstract / Description: The overall goals of this dissertation are to investigate the sustainability of the built environment, holistically, by assessing its Triple Bottom Line (TBL): environmental, economic, and social impacts, as well as propose cost-effective, socially acceptable, and environmentally benign policies using several decision support models. This research is anticipated to transform life cycle assessment (LCA) of the built environment by using a TBL framework, integrated with economic input-output...
Show moreThe overall goals of this dissertation are to investigate the sustainability of the built environment, holistically, by assessing its Triple Bottom Line (TBL): environmental, economic, and social impacts, as well as propose cost-effective, socially acceptable, and environmentally benign policies using several decision support models. This research is anticipated to transform life cycle assessment (LCA) of the built environment by using a TBL framework, integrated with economic input-output analysis, simulation, and multi-criteria optimization tools. The major objectives of the outlined research are to (1) build a system-based TBL sustainability assessment framework for the sustainable built environment, by (a) advancing a national TBL-LCA model which is not available for the United States of America; (b) extending the integrated sustainability framework through environmental, economic, and social sustainability indicators; and (2) develop a system-based analysis toolbox for sustainable decisions including Monte Carlo simulation and multi-criteria compromise programming. When analyzing the total sustainability impacts by each U.S. construction sector, (")Residential Permanent Single and Multi-Family Structures" and "Other Non-residential Structures" are found to have the highest environmental, economic, and social impacts compared to other construction sectors. The analysis results also show that indirect suppliers of construction sectors have the largest sustainability impacts compared to on-site activities. For example, for all U.S. construction sectors, on-site construction processes are found to be responsible for less than 5 % of total water consumption, whereas about 95 % of total water use can be attributed to indirect suppliers. In addition, Scope 3 emissions are responsible for the highest carbon emissions compared to Scope 1 and 2. Therefore, using narrowly defined system boundaries by ignoring supply chain-related impacts can result in underestimation of TBL sustainability impacts of the U.S. construction industry.Residential buildings have higher shares in the most of the sustainability impact categories compared to other construction sectors. Analysis results revealed that construction phase, electricity use, and commuting played important role in much of the sustainability impact categories. Natural gas and electricity consumption accounted for 72% and 78% of the total energy consumed in the U.S. residential buildings. Also, the electricity use was the most dominant component of the environmental impacts with more than 50% of greenhouse gases emitted and energy used through all life stages. Furthermore, electricity generation was responsible for 60% of the total water withdrawal of residential buildings, which was even greater than the direct water consumption in residential buildings. In addition, construction phase had the largest share in income category with 60% of the total income generated through residential building's life cycle. Residential construction sector and its supply chain were responsible for 36% of the import, 40% of the gross operating surplus, and 50% of the gross domestic product. The most sensitive parameters were construction activities and its multiplier in most the sustainability impact categories.In addition, several emerging pavement types are analyzed using a hybrid TBL-LCA framework. Warm-mix Asphalts (WMAs) did not perform better in terms of environmental impacts compared to Hot-mix Asphalt (HMA). Asphamin(&)#174; WMA was found to have the highest environmental and socio-economic impacts compared to other pavement types. Material extractions and processing phase had the highest contribution to all environmental impact indicators that shows the importance of cleaner production strategies for pavement materials. Based on stochastic compromise programming results, in a balanced weighting situation, Sasobit(&)#174; WMA had the highest percentage of allocation (61%), while only socio-economic aspects matter, Asphamin(&)#174; WMA had the largest share (57%) among the WMA and HMA mixtures. The optimization results also supported the significance of an increased WMA use in the United States for sustainable pavement construction. Consequently, the outcomes of this dissertation will advance the state of the art in built environment sustainability research by investigating novel efficient methodologies capable of offering optimized policy recommendations by taking the TBL impacts of supply chain into account. It is expected that the results of this research would facilitate better sustainability decisions in the adoption of system-based TBL thinking in the construction field.
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Date Issued: 2013
Identifier: CFE0005018, ucf:50007
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005018

Title: Stochastic Optimization for Integrated Energy System with Reliability Improvement Using Decomposition Algorithm.
Creator: Huang, Yuping, Zheng, Qipeng, Xanthopoulos, Petros, Pazour, Jennifer, Liu, Andrew, University of Central Florida
Abstract / Description: As energy demands increase and energy resources change, the traditional energy system has beenupgraded and reconstructed for human society development and sustainability. Considerable studies have been conducted in energy expansion planning and electricity generation operations bymainly considering the integration of traditional fossil fuel generation with renewable generation.Because the energy market is full of uncertainty, we realize that these uncertainties have continuously challenged...
Show moreAs energy demands increase and energy resources change, the traditional energy system has beenupgraded and reconstructed for human society development and sustainability. Considerable studies have been conducted in energy expansion planning and electricity generation operations bymainly considering the integration of traditional fossil fuel generation with renewable generation.Because the energy market is full of uncertainty, we realize that these uncertainties have continuously challenged market design and operations, even a national energy policy. In fact, only a few considerations were given to the optimization of energy expansion and generation taking into account the variability and uncertainty of energy supply and demand in energy markets. This usually causes an energy system unreliable to cope with unexpected changes, such as a surge in fuel price, a sudden drop of demand, or a large renewable supply fluctuation. Thus, for an overall energy system, optimizing a long-term expansion planning and market operation in a stochastic environment are crucial to improve the system's reliability and robustness.As little consideration was paid to imposing risk measure on the power management system, this dissertation discusses applying risk-constrained stochastic programming to improve the efficiency,reliability and economics of energy expansion and electric power generation, respectively. Considering the supply-demand uncertainties affecting the energy system stability, three different optimization strategies are proposed to enhance the overall reliability and sustainability of an energy system. The first strategy is to optimize the regional energy expansion planning which focuses on capacity expansion of natural gas system, power generation system and renewable energy system, in addition to transmission network. With strong support of NG and electric facilities, the second strategy provides an optimal day-ahead scheduling for electric power generation system incorporating with non-generation resources, i.e. demand response and energy storage. Because of risk aversion, this generation scheduling enables a power system qualified with higher reliability and promotes non-generation resources in smart grid. To take advantage of power generation sources, the third strategy strengthens the change of the traditional energy reserve requirements to risk constraints but ensuring the same level of systems reliability In this way we can maximize the use of existing resources to accommodate internal or/and external changes in a power system.All problems are formulated by stochastic mixed integer programming, particularly consideringthe uncertainties from fuel price, renewable energy output and electricity demand over time. Taking the benefit of models structure, new decomposition strategies are proposed to decompose the stochastic unit commitment problems which are then solved by an enhanced Benders Decomposition algorithm. Compared to the classic Benders Decomposition, this proposed solution approachis able to increase convergence speed and thus reduce 25% of computation times on the same cases.
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Date Issued: 2014
Identifier: CFE0005506, ucf:50339
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005506

Title: A Systems Approach to Sustainable Energy Portfolio Development.
Creator: Hadian Niasar, Saeed, Reinhart, Debra, Madani Larijani, Kaveh, Wang, Dingbao, Lee, Woo Hyoung, Pazour, Jennifer, University of Central Florida
Abstract / Description: Adequate energy supply has become one of the vital components of human development and economic growth of nations. In fact, major components of the global economy such as transportation services, communications, industrial processes, and construction activities are dependent on adequate energy resources. Even mining and extraction of energy resources, including harnessing the forces of nature to produce energy, are dependent on accessibility of sufficient energy in the appropriate form at the...
Show moreAdequate energy supply has become one of the vital components of human development and economic growth of nations. In fact, major components of the global economy such as transportation services, communications, industrial processes, and construction activities are dependent on adequate energy resources. Even mining and extraction of energy resources, including harnessing the forces of nature to produce energy, are dependent on accessibility of sufficient energy in the appropriate form at the desired location. Therefore, energy resource planning and management to provide appropriate energy in terms of both quantity and quality has become a priority at the global level. The increasing demand for energy due to growing population, higher living standards, and economic development magnifies the importance of reliable energy plans. In addition, the uneven distribution of traditional fossil fuel energy sources on the Earth and the resulting political and economic interactions are other sources of complexity within energy planning. The competition over fossil fuels that exists due to gradual depletion of such sources and the tremendous thirst of current global economic operations for these sources, as well as the sensitivity of fossil fuel supplies and prices to global conditions, all add to the complexity of effective energy planning. In addition to diversification of fossil fuel supply sources as a means of increasing national energy security, many governments are investing in non-fossil fuels, especially renewable energy sources, to combat the risks associated with adequate energy supply. Moreover, increasing the number of energy sources also adds further complication to energy planning. Global warming, resulting from concentration of greenhouse gas emissions in the atmosphere, influences energy infrastructure investments and operations management as a result of international treaty obligations and other regulations requiring that emissions be cut to sustainable levels. Burning fossil fuel, as one of the substantial driving factors of global warming and energy insecurity, is mostly impacted by such policies, pushing forward the implementation of renewable energy polices. Thus, modern energy portfolios comprise a mix of renewable energy sources and fossil fuels, with an increasing share of renewables over time. Many governments have been setting renewable energy targets that mandate increasing energy production from such sources over time. Reliance on renewable energy sources certainly helps with reduction of greenhouse gas emissions while improving national energy security. However, the growing implementation of renewable energy has some limitations. Such energy technologies are not always as cheap as fossil fuel sources, mostly due to immaturity of these energy sources in most locations as well as high prices of the materials and equipment to harness the forces of nature and transform them to usable energy. In addition, despite the fact that renewable energy sources are traditionally considered to be environmentally friendly, compared to fossil fuels, they sometimes require more natural resources such as water and land to operate and produce energy. Hence, the massive production of energy from these sources may lead to water shortage, land use change, increasing food prices, and insecurity of water supplies. In other words, the energy production from renewables might be a solution to reduce greenhouse gas emissions, but it might become a source of other problems such as scarcity of natural resources.The fact that future energy mix will rely more on renewable sources is undeniable, mostly due to depletion of fossil fuel sources over time. However, the aforementioned limitations pose a challenge to general policies that encourage immediate substitution of fossil fuels with renewables to battle climate change. In fact, such limitations should be taken into account in developing reliable energy policies that seek adequate energy supply with minimal secondary effects. Traditional energy policies have been suggesting the expansion of least cost energy options, which were mostly fossil fuels. Such sources used to be considered riskless energy options with low volatility in the absence of competitive energy markets in which various energy technologies are competing over larger market shares. Evolution of renewable energy technologies, however, complicated energy planning due to emerging risks that emanated mostly from high price volatility. Hence, energy planning began to be seen as investment problems in which the costs of energy portfolio were minimized while attempting to manage associated price risks. So, energy policies continued to rely on risky fossil fuel options and small shares of renewables with the primary goal to reduce generation costs. With emerging symptoms of climate change and the resulting consequences, the new policies accounted for the costs of carbon emissions control in addition to other costs. Such policies also encouraged the increased use of renewable energy sources. Emissions control cost is not an appropriate measure of damages because these costs are substantially less than the economic damages resulting from emissions. In addition, the effects of such policies on natural resources such as water and land is not directly taken into account. However, sustainable energy policies should be able to capture such complexities, risks, and tradeoffs within energy planning. Therefore, there is a need for adequate supply of energy while addressing issues such as global warming, energy security, economy, and environmental impacts of energy production processes. The effort in this study is to develop an energy portfolio assessment model to address the aforementioned concerns.This research utilized energy performance data, gathered from extensive review of articles and governmental institution reports. The energy performance values, namely carbon footprint, water footprint, land footprint, and cost of energy production were carefully selected in order to have the same basis for comparison purposes. If needed, adjustment factors were applied. In addition, the Energy Information Administration (EIA) energy projection scenarios were selected as the basis for estimating the share of the energy sources over the years until 2035. Furthermore, the resource availability in different states within the U.S. was obtained from publicly available governmental institutions that provide such statistics. Specifically, the carbon emissions magnitudes (metric tons per capita) for different states were extracted from EIA databases, states' freshwater withdrawals (cubic meters per capita) were found from USGS databases, states' land availability values (square kilometers) were obtained from the U.S. Census Bureau, and economic resource availability (GDP per capita) for different states were acquired from the Bureau of Economic Analysis.In this study, first, the impacts of energy production processes on global freshwater resources are investigated based on different energy projection scenarios. Considering the need for investing on energy sources with minimum environmental impacts while securing maximum efficiency, a systems approach is adopted to quantify the resource use efficiency of energy sources under sustainability indicators. The sensitivity and robustness of the resource use efficiency scores are then investigated versus existing energy performance uncertainties and varying resource availability conditions. The resource use efficiency of the energy sources is then regionalized for different resource limitation conditions in states within the U.S. Finally, a sustainable energy planning framework is developed based on Modern Portfolio Theory (MPT) and Post-Modern Portfolio Theory (PMPT) with consideration of the resource use efficiency measures and associated efficiency risks.In the energy-water nexus investigation, the energy sources are categorized into 10 major groups with distinct water footprint magnitudes and associated uncertainties. The global water footprint of energy production processes are then estimated for different EIA energy mix scenarios over the 2012-2035 period. The outcomes indicate that the water footprint of energy production increases by almost 50% depending on the scenario. In fact, growing energy production is not the only reason for increasing the energy related water footprint. Increasing the share of water intensive energy sources in the future energy mix is another driver of increasing global water footprint of energy in the future. The results of the energies' water footprint analysis demonstrate the need for a policy to reduce the water use of energy generation. Furthermore, the outcomes highlight the importance of considering the secondary impacts of energy production processes besides their carbon footprint and costs. The results also have policy implications for future energy investments in order to increase the water use efficiency of energy sources per unit of energy production, especially those with significant water footprint such as hydropower and biofuels.In the next step, substantial efforts have been dedicated to evaluating the efficiency of different energy sources from resource use perspective. For this purpose, a system of systems approach is adopted to measure the resource use efficiency of energy sources in the presence of trade-offs between independent yet interacting systems (climate, water, land, economy). Hence, a stochastic multi-criteria decision making (MCDM) framework is developed to compute the resource use efficiency scores for four sustainability assessment criteria, namely carbon footprint, water footprint, land footprint, and cost of energy production considering existing performance uncertainties. The energy sources' performances under aforementioned sustainability criteria are represented in ranges due to uncertainties that exist because of technological and regional variations. Such uncertainties are captured by the model based on Monte-Carlo selection of random values and are translated into stochastic resource use efficiency scores. As the notion of optimality is not unique, five MCDM methods are exploited in the model to counterbalance the bias toward definition of optimality. This analysis is performed under (")no resource limitation(") conditions to highlight the quality of different energy sources from a resource use perspective. The resource use efficiency is defined as a dimensionless number in scale of 0-100, with greater numbers representing a higher efficiency. The outcomes of this analysis indicate that despite increasing popularity, not all renewable energy sources are more resource use efficient than non-renewable sources. This is especially true for biofuels and different types of ethanol that demonstrate lower resource use efficiency scores compared to natural gas and nuclear energy. It is found that geothermal energy and biomass energy from miscanthus are the most and least resource use efficient energy alternatives based on the performance data available in the literature. The analysis also shows that none of the energy sources are strictly dominant or strictly dominated by other energy sources. Following the resource use efficiency analysis, sensitivity and robustness analyses are performed to determine the impacts of resource limitations and existing performance uncertainties on resource use efficiency, respectively. Sensitivity analysis indicates that geothermal energy and ethanol from sugarcane have the lowest and highest resource use efficiency sensitivity, respectively. Also, it is found that from a resource use perspective, concentrated solar power (CSP) and hydropower are respectively the most and least robust energy options with respect to the existing performance uncertainties in the literature.In addition to resource use efficiency analysis, sensitivity analysis and robustness analysis, of energy sources, this study also investigates the scheme of the energy production mix within a specific region with certain characteristics, resource limitations, and availabilities. In fact, different energy sources, especially renewables, vary in demand for natural resources (such as water and land), environmental impacts, geographic requirements, and type of infrastructure required for energy production. In fact, the efficiency of energy sources from a resource use perspective is dependent upon regional specifications, so the energy portfolio varies for different regions due to varying resource availability conditions. Hence, the resource use efficiency scores of different energy technologies are calculated based on the aforementioned sustainability criteria and regional resource availability and limitation conditions (emissions, water resources, land, and GDP) within different U.S. states, regardless of the feasibility of energy alternatives in each state. Sustainability measures are given varying weights based on the emissions cap, available economic resources, land, and water resources in each state, upon which the resource use efficiency of energy sources is calculated by utilizing the system of systems framework developed in the previous step. Efficiency scores are graphically illustrated on GIS-based maps for different states and different energy sources. The results indicate that for some states, fossil fuels such as coal and natural gas are as efficient as renewables like wind and solar energy technologies from resource use perspective. In other words, energy sources' resource use efficiency is significantly sensitive to available resources and limitations in a certain location.Moreover, energy portfolio development models have been created in order to determine the share of different energy sources of total energy production, in order to meet energy demand, maintain energy security, and address climate change with the least possible adverse impacts on the environment. In fact, the traditional (")least cost(") energy portfolios are outdated and should be replaced with (")most efficient(") ones that are not only cost-effective, but also environmentally friendly. Hence, the calculated resource use efficiency scores and associated statistical analysis outcomes for a range of renewable and nonrenewable energy sources are fed into a portfolio selection framework to choose the appropriate energy mixes associated with the risk attitudes of decision makers. For this purpose, Modern Portfolio Theory (MPT) and Post-Modern Portfolio Theory (PMPT) are both employed to illustrate how different interpretations of (")risk of return(") yield different energy portfolios. The results indicate that 2012 energy mix and projected world's 2035 energy portfolio are not sustainable in terms of resource use efficiency and could be substituted with more reliable, more effective portfolios that address energy security and global warming with minimal environmental and economic impacts.
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Date Issued: 2013
Identifier: CFE0005001, ucf:50020
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005001

Life Cycle Sustainability Assessment (2)	+ -
Power System (2)	+ -
Sustainable Transportation (2)	+ -
System Dynamics (2)	+ -
Warehousing (2)	+ -

AS/RS (1)	+ -
Alternative Vehicle Technologies (1)	+ -
Application of Six Sigma Methodology in the Iraqi Oil Fields (1)	+ -
Benders Decomposition (1)	+ -
Chance-Constrained Programming (1)	+ -
Change Management (1)	+ -
Classification (1)	+ -
Clustering (1)	+ -
Cold Chain (1)	+ -
Competitiveness (1)	+ -
Conditional Value-at-Risk (1)	+ -
Consensus Clustering (1)	+ -
Control Chart Pattern Recognition (1)	+ -
Control chart pattern recognition (1)	+ -
Corporate Organizations (1)	+ -
Cost-sensitive learning (1)	+ -
Data Mining (1)	+ -
Decomposition (1)	+ -
Dense Storage (1)	+ -
Distributed generation (1)	+ -
Economic Input-Output Analysis (1)	+ -
Electric Vehicles (1)	+ -
Emission (1)	+ -
Energy planning (1)	+ -
Ensemble Methods (1)	+ -

Pazour, Jennifer (18)	+ -
University of Central Florida (18)	+ -
Xanthopoulos, Petros (6)	+ -
Rabelo, Luis (4)	+ -
Zheng, Qipeng (4)	+ -

Oloufa, Amr (3)	+ -
Atia, George (2)	+ -
Elshennawy, Ahmad (2)	+ -
Karwowski, Waldemar (2)	+ -
Mollaghasemi, Mansooreh (2)	+ -
Nam, Boo Hyun (2)	+ -
Nazzal, Dima (2)	+ -
Onat, Nuri (2)	+ -
Tatari, Omer (2)	+ -
Ajayi, Richard (1)	+ -
Al-Deek, Haitham (1)	+ -
Al-Shamkhani, Maher T. (1)	+ -
Asrari, Arash (1)	+ -
Awwad, Mohamed (1)	+ -
Behzadan, Amir (1)	+ -
Buchanan, Patricia (1)	+ -
Chang, Ni-bin (1)	+ -
Elattar, Ahmed (1)	+ -
Geiger, Christopher (1)	+ -
Goodman, Stephen (1)	+ -
Hadian Niasar, Saeed (1)	+ -
Haghtalab, Siavash (1)	+ -
Hajiaghabozorgi, Ali (1)	+ -
Haralambous, Michael (1)	+ -
Huang, Yuping (1)	+ -

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