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(1 - 9 of 9)
- Title
- A REAL OPTION STRATEGIC SCORECARD DECISION FRAMEWORK FOR IT PROJECT SELECTION.
- Creator
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Munoz, Cesar, Rabelo, Luis, University of Central Florida
- Abstract / Description
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ABSTRACT The problem of project selection is of significant importance in management of information systems. Almost $2 trillion is spent worldwide every year on IT projects, with over $600 billion spent in the US alone. Traditionally, managers have being using the classical net present value (NPV) method in conjunction with multicriteria scoring models for ROI analysis and selection of IT project investments The multicriteria models use ad-hoc evaluation criteria to assign priority weights...
Show moreABSTRACT The problem of project selection is of significant importance in management of information systems. Almost $2 trillion is spent worldwide every year on IT projects, with over $600 billion spent in the US alone. Traditionally, managers have being using the classical net present value (NPV) method in conjunction with multicriteria scoring models for ROI analysis and selection of IT project investments The multicriteria models use ad-hoc evaluation criteria to assign priority weights and then rate the alternatives against each criterion. These models have two limitations. First, the criteria and weights are based on subjective judgments, allowing the introduction of politics in the information management decision process and the generation of arbitrary results. Second, the classical approach uses deterministic estimations of the cost, benefits and the returns of the projects, without considering the impact of uncertainty and risk in the business decisions. This research proposed a better alternative for ROI analysis and selection of IT projects using a real option strategic scorecard (ROSS) approach. In contrast with traditional methodologies and previous research work, the ROSS decision framework uses a more comprehensive, axiomatic approach for systematically measuring both the business value and the strategic implications of IT project investments. The ROSS approach integrates in a unified IT project management decision framework the best elements of real option theory, strategic balanced scorecards, Monte Carlo simulations and analytical network processes to fully analyzes the effect of uncertainty and risk in the IT investment decisions. In addition, the ROSS approach complies with the critical success factors that have being identified in the literature for validation of IT decision frameworks. The main benefit of the ROSS approach is to enable managers to better compare and rank projects in the IT portfolio, optimizing the ROI analysis and selection of information system projects.
Show less - Date Issued
- 2006
- Identifier
- CFE0001331, ucf:46975
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001331
- Title
- A Posteriori and Interactive Approaches for Decision-Making with Multiple Stochastic Objectives.
- Creator
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Bakhsh, Ahmed, Geiger, Christopher, Mollaghasemi, Mansooreh, Xanthopoulos, Petros, Wiegand, Rudolf, University of Central Florida
- Abstract / Description
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Computer simulation is a popular method that is often used as a decision support tool in industry to estimate the performance of systems too complex for analytical solutions. It is a tool that assists decision-makers to improve organizational performance and achieve performance objectives in which simulated conditions can be randomly varied so that critical situations can be investigated without real-world risk. Due to the stochastic nature of many of the input process variables in simulation...
Show moreComputer simulation is a popular method that is often used as a decision support tool in industry to estimate the performance of systems too complex for analytical solutions. It is a tool that assists decision-makers to improve organizational performance and achieve performance objectives in which simulated conditions can be randomly varied so that critical situations can be investigated without real-world risk. Due to the stochastic nature of many of the input process variables in simulation models, the output from the simulation model experiments are random. Thus, experimental runs of computer simulations yield only estimates of the values of performance objectives, where these estimates are themselves random variables.Most real-world decisions involve the simultaneous optimization of multiple, and often conflicting, objectives. Researchers and practitioners use various approaches to solve these multiobjective problems. Many of the approaches that integrate the simulation models with stochastic multiple objective optimization algorithms have been proposed, many of which use the Pareto-based approaches that generate a finite set of compromise, or tradeoff, solutions. Nevertheless, identification of the most preferred solution can be a daunting task to the decision-maker and is an order of magnitude harder in the presence of stochastic objectives. However, to the best of this researcher's knowledge, there has been no focused efforts and existing work that attempts to reduce the number of tradeoff solutions while considering the stochastic nature of a set of objective functions.In this research, two approaches that consider multiple stochastic objectives when reducing the set of the tradeoff solutions are designed and proposed. The first proposed approach is an a posteriori approach, which uses a given set of Pareto optima as input. The second approach is an interactive-based approach that articulates decision-maker preferences during the optimization process. A detailed description of both approaches is given, and computational studies are conducted to evaluate the efficacy of the two approaches. The computational results show the promise of the proposed approaches, in that each approach effectively reduces the set of compromise solutions to a reasonably manageable size for the decision-maker. This is a significant step beyond current applications of decision-making process in the presence of multiple stochastic objectives and should serve as an effective approach to support decision-making under uncertainty.
Show less - Date Issued
- 2013
- Identifier
- CFE0004973, ucf:49574
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004973
- Title
- Selective Multivariate Applications in Forensic Science.
- Creator
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Rinke, Caitlin, Sigman, Michael, Campiglia, Andres, Yestrebsky, Cherie, Kuebler, Stephen, Richardson, Martin, University of Central Florida
- Abstract / Description
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A 2009 report published by the National Research Council addressed the need for improvements in the field of forensic science. In the report emphasis was placed on the need for more rigorous scientific analysis within many forensic science disciplines and for established limitations and determination of error rates from statistical analysis. This research focused on multivariate statistical techniques for the analysis of spectral data obtained for multiple forensic applications which include...
Show moreA 2009 report published by the National Research Council addressed the need for improvements in the field of forensic science. In the report emphasis was placed on the need for more rigorous scientific analysis within many forensic science disciplines and for established limitations and determination of error rates from statistical analysis. This research focused on multivariate statistical techniques for the analysis of spectral data obtained for multiple forensic applications which include samples from: automobile float glasses and paints, bones, metal transfers, ignitable liquids and fire debris, and organic compounds including explosives. The statistical techniques were used for two types of data analysis: classification and discrimination. Statistical methods including linear discriminant analysis and a novel soft classification method were used to provide classification of forensic samples based on a compiled library. The novel soft classification method combined three statistical steps: Principal Component Analysis (PCA), Target Factor Analysis (TFA), and Bayesian Decision Theory (BDT) to provide classification based on posterior probabilities of class membership. The posterior probabilities provide a statistical probability of classification which can aid a forensic analyst in reaching a conclusion. The second analytical approach applied nonparametric methods to provide the means for discrimination between samples. Nonparametric methods are performed as hypothesis test and do not assume normal distribution of the analytical figures of merit. The nonparametric permutation test was applied to forensic applications to determine the similarity between two samples and provide discrimination rates. Both the classification method and discrimination method were applied to data acquired from multiple instrumental methods. The instrumental methods included: Laser Induced-Breakdown Spectroscopy (LIBS), Fourier Transform Infrared Spectroscopy (FTIR), Raman spectroscopy, and Gas Chromatography-Mass Spectrometry (GC-MS). Some of these instrumental methods are currently applied to forensic applications, such as GC-MS for the analysis of ignitable liquid and fire debris samples; while others provide new instrumental methods to areas within forensic science which currently lack instrumental analysis techniques, such as LIBS for the analysis of metal transfers. The combination of the instrumental techniques and multivariate statistical techniques is investigated in new approaches to forensic applications in this research to assist in improving the field of forensic science.
Show less - Date Issued
- 2012
- Identifier
- CFE0004628, ucf:49942
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004628
- Title
- DRIVE-BASED MODELING AND VISUALIZATION OF CREW RACE STRATEGY AND PERFORMANCE.
- Creator
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Cornett, Jeffrey, Bush, Pamela, University of Central Florida
- Abstract / Description
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Crew race strategy is typically formulated by coaches based on rowing tradition and years of experience. However, coaching strategies are not generally supported by empirical evidence and decision-support models. Previous models of crew race strategy have been constrained by the sparse information published on crew race performance (quarterly 500-meter splits). Empirical research has merely summarized which quarterly splits averaged the fastest and slowest relative to the other splits and...
Show moreCrew race strategy is typically formulated by coaches based on rowing tradition and years of experience. However, coaching strategies are not generally supported by empirical evidence and decision-support models. Previous models of crew race strategy have been constrained by the sparse information published on crew race performance (quarterly 500-meter splits). Empirical research has merely summarized which quarterly splits averaged the fastest and slowest relative to the other splits and relative to the average speed of the other competitors. Video records of crew race world championships provide a rich source of data for those capable and patient enough to mine this level of detail. This dissertation is based on a precise frame-by-frame video analysis of five world championship rowing finals. With six competing crews per race, a database of 75 race-pair duels was compiled that summarizes race positioning, competitive drives, and relative stroke rates at 10-meter intervals recorded with photo-finish precision (30 frames per second). The drive-based research pioneered in this dissertation makes several contributions to understanding the dynamics of crew race strategy and performance: 1) An 8-factor conceptual model of crew race performance. 2) A generic drive model that decomposes how pairs of crews duel in a race. 3) Graphical summaries of the rates and locations of successful and unsuccessful drives. 4) Contour lines of the margins that winning crews hold over the course of the race. 5) Trend lines for what constitutes a probabilistically decisive lead as a function of position along the course, seconds behind the leader, and whether the trailing crew is driving. This research defines a new drive-based vocabulary for evaluating crew race performance for use by coaches, competitors and race analysts. The research graphically illustrates situational parameters helpful in formulating race strategy and guiding real-time decision-making by competitors. This research also lays the foundation for future industrial engineering decision-support models and associated parameters as applied to race strategy and tactics.
Show less - Date Issued
- 2008
- Identifier
- CFE0002428, ucf:47745
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002428
- Title
- OPTIMIZING THE LEVEL OF CUSTOMIZATION FOR PRODUCTS IN MASS CUSTOMIZATION SYSTEMS.
- Creator
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Spahi, Sami, Hosni, Yasser, University of Central Florida
- Abstract / Description
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Mass customization (MC) was developed to capitalize on the combined benefits of economies of scale and economies of scope. Balancing the tradeoffs involved in an MC system warrants the determination of the degree or the extent of customization. Most of the literature views the degree of customization as how early or how far the customer is integrated in the production cycle, which is defined as the order decoupling point. In this study we are addressing the degree of customization from a...
Show moreMass customization (MC) was developed to capitalize on the combined benefits of economies of scale and economies of scope. Balancing the tradeoffs involved in an MC system warrants the determination of the degree or the extent of customization. Most of the literature views the degree of customization as how early or how far the customer is integrated in the production cycle, which is defined as the order decoupling point. In this study we are addressing the degree of customization from a product structural perspective. There are two objectives in this research. The first is to develop a unit of measurement for the degree of customization of a product in an MC system. The second is to construct an optimization model to determine the level of customization that would best satisfy the organizational goals. The term "Magnitude of Customization" (MOC) has been introduced as a measuring unit for the degree of customization on a customization scale (CS). The MOC is based on the number of module options or the extent of customizable features per component in a product. To satisfy the second objective, an analytical model based on preemptive goal programming was developed. The model optimizes the solution as to how far an organization should customize a product to best satisfy its strategic goals. The model considers goals such as increasing the market share, and attaining a higher level of customer satisfaction, while keeping the risk or budget below a certain amount. A step-by-step algorithm is developed for the model application. A case study of an aluminum windows and doors company is used to verify and validate the model. A double panel sliding window is selected as the subject of our study. Information related to company goals and objectives vis-à-vis customization is gathered, through interviews and questionnaires, from the upper management including Operations, Marketing, and Finance Departments. The Window design and technical information are collected from the Manufacturing Department. The model and its solution provided specific recommendations on what to customize and to what degree to best satisfy primary strategic goals for the organization. Results from the model application shows that the company is able to meet the five goals that they had identified with two goals having a deviation of 4.7% and 6.6% from the targets. To achieve the stated goals, the model recommends an overall degree of customization of approximately 32.23% and delineates that to the component and feature levels. For validation, the model results are compared to the actual status of the company and the manufacturer's recommendation without prior information about the model outcome. The average difference, for attaining the same goals, is found to be 6.05%, at a standard deviation of 6.02% and variance of 36.29%, which is considered adequately close. The proposed model presents a framework that combines various research efforts into a flexible but encompassing method that can provide decision-makers with essential production planning guidelines in an MC setup. Finally, suggestions are provided as to how the model can be expanded and refined to include goal formulations that accommodate potential MC systems and technology advances. To the best of our knowledge, this research is a pioneer in quantifying customization in an MC environment and relating it to the organizational goals through modeling and optimization.
Show less - Date Issued
- 2008
- Identifier
- CFE0002197, ucf:47902
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002197
- Title
- Applying Machine Learning Techniques to Analyze the Pedestrian and Bicycle Crashes at the Macroscopic Level.
- Creator
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Rahman, Md Sharikur, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Yan, Xin, University of Central Florida
- Abstract / Description
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This thesis presents different data mining/machine learning techniques to analyze the vulnerable road users' (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based...
Show moreThis thesis presents different data mining/machine learning techniques to analyze the vulnerable road users' (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based on the Statewide Traffic Analysis Zones (STAZ) level crash count data for both pedestrian and bicycle from the state of Florida for the year of 2010 to 2012. The model results highlight the most significant predictor variables for pedestrian and bicycle crash count in terms of three broad categories: traffic, roadway, and socio demographic characteristics. Furthermore, spatial predictor variables of neighboring STAZ were utilized along with the targeted STAZ variables in order to improve the prediction accuracy of both DTR models. The DTR model considering spatial predictor variables (spatial DTR model) were compared without considering spatial predictor variables (aspatial DTR model) and the models comparison results clearly found that spatial DTR model is superior model compared to aspatial DTR model in terms of prediction accuracy. Finally, this study contributed to the safety literature by applying three ensemble techniques (Bagging, Random Forest, and Boosting) in order to improve the prediction accuracy of weak learner (DTR models) for macro-level crash count. The model's estimation result revealed that all the ensemble technique performed better than the DTR model and the gradient boosting technique outperformed other competing ensemble technique in macro-level crash prediction model.
Show less - Date Issued
- 2018
- Identifier
- CFE0007358, ucf:52103
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007358
- Title
- AUTOMATIC GENERATION OF SUPPLY CHAIN SIMULATION MODELS FROM SCOR BASED ONTOLOGIES.
- Creator
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Cope, Dayana, Sepulveda, Jose, University of Central Florida
- Abstract / Description
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In today's economy of global markets, supply chain networks, supplier/customer relationship management and intense competition; decision makers are faced with a need to perform decision making using tools that do not accommodate the nature of the changing market. This research focuses on developing a methodology that addresses this need. The developed methodology provides supply chain decision makers with a tool to perform efficient decision making in stochastic, dynamic and distributed...
Show moreIn today's economy of global markets, supply chain networks, supplier/customer relationship management and intense competition; decision makers are faced with a need to perform decision making using tools that do not accommodate the nature of the changing market. This research focuses on developing a methodology that addresses this need. The developed methodology provides supply chain decision makers with a tool to perform efficient decision making in stochastic, dynamic and distributed supply chain environments. The integrated methodology allows for informed decision making in a fast, sharable and easy to use format. The methodology was implemented by developing a stand alone tool that allows users to define a supply chain simulation model using SCOR based ontologies. The ontology includes the supply chain knowledge and the knowledge required to build a simulation model of the supply chain system. A simulation model is generated automatically from the ontology to provide the flexibility to model at various levels of details changing the model structure on the fly. The methodology implementation is demonstrated and evaluated through a retail oriented case study. When comparing the implementation using the developed methodology vs. a "traditional" simulation methodology approach, a significant reduction in definition and execution time was observed.
Show less - Date Issued
- 2008
- Identifier
- CFE0002009, ucf:47625
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002009
- Title
- Life Cycle Sustainability Assessment Framework for the U.S. Built Environment.
- Creator
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Kucukvar, Murat, Tatari, Mehmet, Oloufa, Amr, Behzadan, Amir, Al-Deek, Haitham, Pazour, Jennifer, University of Central Florida
- Abstract / Description
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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.
Show less - Date Issued
- 2013
- Identifier
- CFE0005018, ucf:50007
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005018
- Title
- Uncovering The Sub-Text: Presidents' Emotional Expressions and Major Uses of Force.
- Creator
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Assaf, Elias, Houghton, David, Kim, Myunghee, Dolan, Thomas, University of Central Florida
- Abstract / Description
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The global context of decision making continues to adapt in response to international threats. Political psychologists have therefore considered decision making processes regarding major uses of force a key area of interest. Although presidential personality has been widely studied as a mitigating factor in the decision making patterns leading to uses of force, traditional theories have not accounted for the emotions of individuals as they affect political actions and are used to frame public...
Show moreThe global context of decision making continues to adapt in response to international threats. Political psychologists have therefore considered decision making processes regarding major uses of force a key area of interest. Although presidential personality has been widely studied as a mitigating factor in the decision making patterns leading to uses of force, traditional theories have not accounted for the emotions of individuals as they affect political actions and are used to frame public perception of the use of force. This thesis therefore measures expressed emotion and cognitive expressions in the form of expressed aggression, passivity, blame, praise, certainty, realism, and optimism as a means of predicting subsequent major uses of force. Since aggression and blame are precipitated by anger and perceived vulnerability, they are theorized to foreshadow increased uses of force (Gardner and Moore 2008). Conversely, passivity and praise are indicative of empathy and joy respectively, and are not expected to precede aggressive behavior conducted to maintain emotional regulation (Roberton, Daffer, and Bucks 2012). Additionally, the three cognitive variables of interest expand on existing literature on beliefs and decision making expounded by such authors as Walker (2010), Winter (2003) and Hermann (2003). DICTION 6.0 is used to analyze all text data of presidential news conferences, candidate debates, and State of the Union speeches given between 1945 and 2000 stored by The American Presidency Project (Hart and Carroll 2012). Howell and Pevehouse's (2005) quantitative assessment of quarterly U.S. uses of force between 1945 and 2000 is employed as a means of quantifying instances of major uses of force. Results show systematic differences among the traits expressed by presidents, with most expressions staying consistent across spontaneous speech contexts. Additionally, State of the Union speeches consistently yielded the highest scores across the expressed traits measured; supporting the theory that prepared speech is used to emotionally frame situations and setup emotional interpretations of events to present to the public. Time sensitive regression analyses indicate that expressed aggression within the context of State of the Union Addresses is the only significant predictor of major uses of force by the administration. That being said, other studies may use the comparative findings presented herein to further establish a robust model of personality that accounts for individual dispositions toward emotional expression as a means of framing the emotional interpretation of events by audiences.
Show less - Date Issued
- 2014
- Identifier
- CFE0005300, ucf:50513
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005300
