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Dissertations / Theses on the topic 'Process uncertainty'
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1
Arellano-Garcia, Harvey. "Chance constrained optimization of process systems under uncertainty." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=982225652.
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Ierapetritou, Marianthi. "Optimization approaches for process engineering problems under uncertainty." Thesis, Imperial College London, 1995. http://hdl.handle.net/10044/1/7187.
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Production systems typically involve significant uncertainty in their operation due to either external or internal sources. The existence of uncertainty transforms conventional deterministic process models to stochastic/parametric problems, the solution of which requires the application of specialized optimization techniques. The main objective of this thesis is to develop suitable algorithms and numerical techniques for the efficient solution of process engineering problems involving uncertain parameters. Based on modelling issues regarding uncertainty classification (deterministic and stochastic uncertain parameters) and design objectives with respect to uncertainty (fixed degree of flexibility and optimal degree of flexibility), a unified multiperiod/stochastic two-stage optimization formulation is proposed and a decomposition based algorithmic procedure is developed for its efficient solution. The proposed algorithm forms the basis for addressing process design problems, planning and scheduling problems and problems related to behavioural analysis under uncertainty. For batch plant design problems involving uncertainty in the description of process parameters such as transfer coefficients, kinetic constants, etc., as well as variability of external parameters such as product demand, economic cost data etc., the exploitation of the special structure coupled with the relaxation of feasibility requirement regarding product demands enables the transformation of the stochastic two-stage programming problems to a single optimization model where the structure of the deterministic problem is fully preserved. For the case of continuous size of equipments, an efficient global optimization procedure is proposed, whereas for the case of discrete equipment sizes the algorithm reduces to the solution of a mixed integer linear programming problem. For short term production planning and long-range planning problems including capacity expansion options, the application of the proposed approach results in the optimal production plan (i. e. process utilization levels, purchases and sales of materials) and/or an optimal capacity expansion policy that maximize an expected profit and ensure an optimal level of future feasibility. Finally, an attempt to address the question of the future "value" of uncertainty is presented based on the concept of value of perfect information. It is shown that the proposed algorithmic developments can be effectively extended to include both the solution of the here-and-now and the wait-and-see models in order to analyze and integrate the
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Kallias, Antonios. "Managing uncertainty in the process of going public." Thesis, University of Sussex, 2016. http://sro.sussex.ac.uk/id/eprint/60423/.
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This thesis explores the potential of novel mechanisms towards the reduction of issuers' ex ante uncertainty in the process of going public: i) the recruitment of directors with exceptional academic backgrounds and ii) obtaining credit ratings. Given the information scarcity in the private domain, IPO firms can use these strategies to provide investors with solid, readily identifiable benchmarks to assess their standing. Notwithstanding whether these informational cues are associated with positive or negative prospects, they cause a significant portion of uncertainty in valuation to subside. Ultimately, this should act to constrain the phenomenon of IPO underpricing causing firms to claim a larger portion of the surplus value created on the issue day. First, we examine whether CEO educational and professional attainments are associated with short-run IPOs performance. We find that returns are negatively associated with Ivy-League education, the existence of at least one University degree and the total number of qualifications. After controlling for endogeneity and self-selection bias, the results show that at the graduate level of education the Master of Arts, the MBA, the Juris and Medical Doctor titles exhibit negative relation with the money left on the table. The same is true for any professional qualification. It is also reported that only in the case of the PhD title the Nobel Elite group of Universities outperforms the rest of the sample. Second, we examine the effect of multiple credit ratings on IPO performance. The evidence comes from the U.S. and shows that the acquisition of credit ratings constitutes a valid investment decision for the issuing firm as it leaves less money on the table. Both individual as well as any combination of ratings from the three largest agencies associates with lower underpricing. This effect exacerbates with higher grade levels which are also found to decrease initial returns. Additionally, rated IPOs systematically experience negative filing price revisions. The results offer new insight to the facilitation of the going public process. Finally, we contribute to the large literature associating IPOs with earnings management. In this respect, we explore a special niche, i.e. politically connected firms. A priori, these issuers can be expected to refrain from discretionary accruals manipulation to avoid causing discontent to their contacts. Alternatively, the case may be that the powerful acquaintances fuel managers with overconfidence which permeates the financial statements. Assembling a hand-collected database on firms' political donations, we come up with strong support for the latter conjecture. In particular lobbying activity and candidate campaign financing are both shown to be among the important determinants of aggressiveness in reporting. Our findings tie in with a growing body of literature showing businesses actively involved in politics to be prone to abuses and professional misconduct.
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Hall, James William. "Uncertainty management for coastal defence systems." Thesis, University of Bristol, 1999. http://hdl.handle.net/1983/9b1c8d07-24f0-48b9-bb7f-73d8d7c40ae6.
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Charitopoulos, Vasileios. "Uncertainty-aware integration of control with process operations and multi-parametric programming under global uncertainty." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10061518/.
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Advanced decision making in the process industries requires efficient use of information available at the different hierarchical levels. However, given the time and decision space sparsity the consideration of such integrated problems poses a plethora of challenges. It is goal of the present thesis to present some recent algorithmic and modelling developments with special focus on the uncertainty aware integration of control with process operations. To this end, the thesis comprises of two parts that are orchestrated towards the attainment of the aforestated goal. The first part discusses theoretical and algorithmic advances in the field of multiparametric programming. Initially, the case of multi-parametric linear programs under simultaneous variations in the left-handside, right-handside of the constraints and the objective function's coefficients is examined. For the first time theoretical characterisation of the explicit solution is proven while an algorithm for their exact computation is proposed. Later on, the aforementioned algorithm is extended to the mixed-integer case where problems of process synthesis and scheduling under global uncertainty are studied. Next, the concept of multi-setpoint explicit controllers and their potential in the context of enterprise wide optimisation problems is introduced. While a prototype implementation of the aforementioned works is also discussed. The second part is dedicated to the development of a systematic framework for the uncertainty aware integration of process planning, scheduling and control (iPSC) of continuous processes. Initially, a Traveling Salesman Problem based formulation is presented and a decomposition method for the deterministic case is proposed. Next, the multi-setpoint explicit controllers developed in the first part of the thesis, enable the development of a reactive closed-loop framework for the iPSC. Ultimately, proactive and reactive approaches are employed in order to instantiate the uncertainty aware iPSC while Monte-Carlo simulations are conducted to evaluate the robustness of the proposed framework.
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Oakley, Jeremy. "Bayesian uncertainty analysis for complex computer codes." Thesis, University of Sheffield, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322915.
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Dua, Vivek. "Parametric programming techniques for process engineering problems under uncertainty." Thesis, Imperial College London, 2000. http://hdl.handle.net/10044/1/7960.
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Randles, Daniel. "The shared psychological process underlying different forms of uncertainty." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/49996.
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How do people react when their meaningful worldviews are violated? What does it even mean to experience a lack of meaning? Drawing on work from both social and cognitive psychology, I advance two main hypotheses. First, humans employ a single domain-general process for recognizing and interpreting all violations of meaning and unexpected experiences. Second, as a result of this generality, all violations of meaning can trigger responses that have more to do with this broad process than with the specific problem at hand. Eight studies support these predictions from a number of methodological approaches. Experiences as superficially different as cognitive dissonance, mortality salience, and viewing surreal art all motivate people to affirm important beliefs that are not directly relevant to the experience. Acetaminophen, a drug known to inhibit physical pain and feelings of rejection, also prevents this motivation to affirm following meaning violations. In an ERP paradigm, acetaminophen inhibits activation associated with consciously recognizing that a mistake was made. Finally, these effects appear to occur spontaneously during everyday moments and are not restricted solely to artificial laboratory experiments. These findings speak to a broad process for identifying mismatches between one’s mental model and reality. Discussion focuses on the implications of this process for studying a range of experiences, including uncertainty, meaning, goal frustration, dissonance, and existential anxiety.Arts, Faculty of
Psychology, Department of
Graduate
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陳頌富 and Chung-fu Leslie Chan. "Machining process selection and sequencing under conditions of uncertainty." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31214927.
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Stone, Nicola. "Gaussian process emulators for uncertainty analysis in groundwater flow." Thesis, University of Nottingham, 2011. http://eprints.nottingham.ac.uk/11989/.
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In the field of underground radioactive waste disposal, complex computer models are used to describe the flow of groundwater through rocks. An important property in this context is transmissivity, the ability of the groundwater to pass through rocks, and the transmissivity field can be represented by a stochastic model. The stochastic model is included in complex computer models which determine the travel time for radionuclides released at one point to reach another. As well as the uncertainty due to the stochastic model, there may also be uncertainties in the inputs of these models. In order to quantify the uncertainties, Monte Carlo analyses are often used. However, for computationally expensive models, it is not always possible to obtain a large enough sample to provide accurate enough uncertainty analyses. In this thesis, we present the use of Bayesian emulation methodology as an alternative to Monte Carlo in the analysis of stochastic models. The idea behind Bayesian emulation methodology is that information can be obtained from a small number of runs of the model using a small sample from the input distribution. This information can then be used to make inferences about the output of the model given any other input. The current Bayesian emulation methodology is extended to emulate two statistics of a stochastic computer model; the mean and the distribution function of the output. The mean is a simple output statistic to emulate and provides some information about how the output changes due to changes in each input. The distribution function is more complex to emulate, however it is an important statistic since it contains information about the entire distribution of the outputs. Distribution functions of radionuclide travel times have been used as part of risk analyses for underground radioactive waste disposal. The extended methodology is presented using a case study.
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Warren, Adam L. "Sequential decision-making under uncertainty /." *McMaster only, 2004.
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Luthfa, Karim Sabrina. "The Uncertainty-Embedded Innovation Process : A study of how uncertainty emerges in the innovation process and of how firms address that to create novelty." Doctoral thesis, Högskolan i Halmstad, Centrum för innovations-, entreprenörskaps- och lärandeforskning (CIEL), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-33850.
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Despite much discussion in the literature of uncertainties in relation to the innovation process, there is little knowledge of how they emerge in this process. This thesis accordingly aims to understand how uncertainty emerges in the innovation process and how firms address that uncertainty to create novelty from the process. Uncertainty is embedded in the innovation process (Jalonen, 2012), which implies that it is not only a factor affecting the innovation process but also an outcome of the process itself. To fulfil the purpose of this study, it is important to understand how the innovation process unfolds over time. It is well established that innovation is a process of recombining resources (Schumpeter, 1934) through the performing and linking of certain activities in sequence (Richardson, 1972; Dubois, 1994; Bankvall, 2011) by various actors (i.e., firms and organizations) in a network context (Håkansson and Olsen, 2012; Lampela, 2012; Love and Roper, 2001; Pittaway et al., 2004; Powell et al., 1996). To fulfil the purpose of this study, the following research question has been asked: How and why do actors undertake and link resource recombination activities in a network context, thereby managing uncertainties in the innovation process? The thesis investigates the innovation process in two companies. One of the companies had completed its innovation journey and the other had almost done so. The discussion gives a detailed account of: the activities these companies performed alone and jointly with their partners in a network context; the resources they exchanged with each other and recombined to bring new solutions to the market; the uncertainties created in the process of recombining the resources; and the activities they undertook in response to address these uncertainties. The innovation process in the case companies is analysed in light of a conceptual model developed here based on Dubois’ (1994) “end product related activity structure model”, Håkansson’s (1987) “ARA model/network model”, and Goldratt’s (1997) “critical chain concept”. This study identifies the conditions under which uncertainties emerged in the innovation process in the studied companies. One of the significant conditions was resource unavailability, which was caused by actors’ reluctance to share resources, prohibition by government policy, and the resources’ own conflicting conditions and internal resistance (Håkansson and Snehota, 1995; Håkansson and Waluszewski, 2002; Waluszewski, 2004). Resource unavailability caused inertial and repetitive activities and delayed the process of producing an outcome, having such an impact on the activities under the condition of path dependency (Arthur, 1994; David, 2000). Another observed condition was the actors’ lack of knowledge of resource combination (Jalonen, 2011). A type of uncertainty that seriously affects the outcome of the innovation process is the activity void, a situation in which no activity is taking place. Activity voids are created from resource unavailability either by an actor’s reluctance to share resources or by the outcome of combining conflicting resource properties. The outcome of the innovation process is therefore affected by the key actor’s attempt to reduce the activity void by making compromises at the three levels, interplay among which construct the process, i.e., actors, resources, and activities. To manage uncertainties, managers make many compromises when they perform and link various activities. Although the underlying motivation for making compromises is rational, it is boundedly rational (Simon, 1957) because by making compromises, managers forego expectations of having all the properties or of being able to plan, undertake, and link activities as intended. This study also reveals that sometimes actors prefer not to make compromises despite knowing that this might cost a great deal. Accordingly, the findings suggest that compromises made within a working relationship allow actors to produce novelty without deviating from the desired path by ensuring access to resources and partners’ abilities. On the contrary, compromises not made in the relationship can threaten actors’ ability to produce the desired novelty, as the exchange of partners’ resources and abilities is hindered in a poor relationship. Compromises made in resource configuration and activities threaten actors’ ability to produce the desired novelty by limiting their choices, while compromises not made in resource configuration and activities allow actors to produce the desired novelty without deviation.
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Olsson, Rolf. "MANAGING PROJECT UNCERTAINTY BY USING AN ENHANCED RISK MANAGEMENT PROCESS." Doctoral thesis, Mälardalen University, Department of Innovation, Design and Product Development, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-160.
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An increasing number of companies are focusing their efforts on project management. Project management is frequently used as an enabler for meeting an uncertain and turbulent environment. Consequently, the overall effectiveness of the project management process is essential for long-term profitability. The aim and final effects of project management are to predict the outcome, i.e. cost, time and quality. However, uncertainty is inherent in the objectives of the project itself, as we use assumptions and expectations in defining and realizing the outcome of the project. A project’s ability to identify and react to uncertainty will influence the outcome of the project. Presently, risk management processes exist in several forms and are often used to manage uncertainty. However, it is frequently argued in academia as well as for the practitioner that risk management does not live up to expected results.
The overall objective of this research is to improve the process for managing risks and opportunities within a project organization. The research starts from the single project view, followed by the strategic link to business strategy by including the project portfolio management perspective. Finally, the research focuses on opportunities and the ability of a project to realize them. Thus, the research questions addressed concern how risk is conceived in a theoretical global context and how this would assist in developing a methodology for risk management in an international project organization. They also involve how risk management within a project portfolio could be conducted and its effectiveness measured. Finally, the research questions also address how the management of opportunities could be improved.
This research includes the development of four methodologies, based on industrial need. A holistic approach with a systems perspective has been used in order to handle the complexity of the research task. Both empirical and theoretical material has been used for developing the proposed methodologies. The developed methodologies for project risk management and the measures of its effectiveness have been tested and improved over a five-year period within the complete case company. Subsequently, two of them were implemented.
The developed methodologies show that the risk management process in a single project does not foster learning and is not directly applicable within a portfolio of projects. Furthermore, the risk management process is not able to address all types of uncertainty. The project manager is a major factor in an effective management of uncertainty. When identifying and managing opportunity, having the ability to create a holistic view, to oversee both customer expectations, and to communicate project related information are important factors. Furthermore, the implementation also showed that it is actually possible, through the consistent use of a risk management process, to develop a cultural behavior within an organization that is much more preventive and proactive than before.
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Juutilainen, I. (Ilmari). "Modelling of conditional variance and uncertainty using industrial process data." Doctoral thesis, University of Oulu, 2006. http://urn.fi/urn:isbn:9514282620.
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Abstract
This thesis presents methods for modelling conditional variance and uncertainty of prediction at a query point on the basis of industrial process data. The introductory part of the thesis provides an extensive background of the examined methods and a summary of the results. The results are presented in detail in the original papers.
The application presented in the thesis is modelling of the mean and variance of the mechanical properties of steel plates. Both the mean and variance of the mechanical properties depend on many process variables. A method for predicting the probability of rejection in a quali?cation test is presented and implemented in a tool developed for the planning of strength margins. The developed tool has been successfully utilised in the planning of mechanical properties in a steel plate mill.
The methods for modelling the dependence of conditional variance on input variables are reviewed and their suitability for large industrial data sets are examined. In a comparative study, neural network modelling of the mean and dispersion narrowly performed the best.
A method is presented for evaluating the uncertainty of regression-type prediction at a query point on the basis of predicted conditional variance, model variance and the effect of uncertainty about explanatory variables at early process stages. A method for measuring the uncertainty of prediction on the basis of the density of the data around the query point is proposed. The proposed distance measure is utilised in comparing the generalisation ability of models. The generalisation properties of the most important regression learning methods are studied and the results indicate that local methods and quadratic regression have a poor interpolation capability compared with multi-layer perceptron and Gaussian kernel support vector regression.
The possibility of adaptively modelling a time-varying conditional variance function is disclosed. Two methods for adaptive modelling of the variance function are proposed. The background of the developed adaptive variance modelling methods is presented.
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Bansal, Vikrant. "Analysis, design and control optimization of process systems under uncertainty." Thesis, Imperial College London, 2000. http://hdl.handle.net/10044/1/8212.
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Ryu, Jun-Hyung. "Design and operation of enterprise-wide process networks under uncertainty." Thesis, Imperial College London, 2003. http://hdl.handle.net/10044/1/7861.
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Acevedo, Mascarus Joaquin. "Parametric and stochastic programming algorithms for process synthesis under uncertainty." Thesis, Imperial College London, 1996. http://hdl.handle.net/10044/1/7903.
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Wong, Pang Hui. "EVALUATION OF A PRODUCT DEVELOPMENT PROCESS THROUGH UNCERTAINTY ANALYSIS TECHNIQUES." MSSTATE, 2003. http://sun.library.msstate.edu/ETD-db/theses/available/etd-07072003-234911/.
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For any product development process, limited time and resources are always a focus for the engineer. However, will the overall program goals be achieved with the provided time and resources? Uncertainty analysis is a tool that is capable of providing the answer to that question. Product development process uncertainty analysis employs previous knowledge in modeling, experimentation, and manufacturing in an innovative approach for analyzing the entire process. This research was initiated with a pilot project, a four-bar-slider mechanism, and an uncertainty analysis was completed for each individual product development step. The uncertainty of the final product was then determined by combining uncertainties from the individual steps. The uncertainty percentage contributions of each term to the uncertainty of the final product were also calculated. The combination of uncertainties in the individual steps and calculation of the percentage contributions of the terms have not been done in the past. New techniques were developed to evaluate the entire product development process in an uncertainty sense. The techniques developed in this work will be extended to other processes in future work.
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Girard, Agathe. "Approximate methods for propagation of uncertainty with Gaussian process models." Thesis, University of Glasgow, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407783.
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Chatfield, Marion J. "Uncertainty of variance estimators in analytical and process variability studies." Thesis, University of Southampton, 2018. https://eprints.soton.ac.uk/422240/.
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This thesis demonstrates that the half-t distribution is the prior of choice for estimating uncertainty of variance estimators in routine analysis of analytical and process variance components studies. Industrial studies are often performed to estimate sources of variation e.g. to improve and quantify measurement or process capability. Understanding the uncertainty of those estimators is important, especially for small studies. A Bayesian analysis is proposed – providing a flexible methodology which easily copes with the complex and varied nature of the studies and the varied quantities of interest. The prior is a fundamental component of a Bayesian analysis. The choice of prior is appraised and the coverage of the credible intervals obtained using six families of priors is assessed. A half-t prior (with several degrees of freedom) on the standard deviation is recommended in preference to a uniform or half-Cauchy prior, when some information exists on the magnitude of variability ‘core’ to the process or analytical method. Whilst a half-t prior has been previously proposed, through extensive simulation it is demonstrated that it is the prior of choice for estimating uncertainty of variance estimators in routine analysis of analytical and process variation studies. The coverage of 95% credible intervals for variance components and total variance is 93% (approximately) or above across a range of realistic scenarios. Other priors investigated, including Jeffreys’, a FLAT prior and inverse gamma distributions on stratum variances available in PROC MIXED1 in the SAS/STAT® software, are less satisfactory. This evaluation is novel: for one-way variance component designs there is very limited evaluation of the half-t prior when estimating the uncertainty of the variance component estimators; for the two-way or more complex none has been found. Since the coverage issues were primarily for the mid-level variance component, evaluation of designs more complex than one-way is important. Highest posterior density intervals are recommended with the metric of the parameter being important. Additionally, a scale based on the intra-class correlation coefficient is proposed for plotting the credible intervals.
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Brown, Robert G. "A risk management process for complex projects." Thesis, This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-07212009-040553/.
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Wilson, Duncan John. "Classification of defects using uncertainty in industrial web inspection." Thesis, University of London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286894.
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Higgins, Paul Anthony. "Reducing uncertainty in new product development." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/20273/1/Paul_Higgins_Thesis.pdf.
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Research and Development engineering is at the corner stone of humanity’s evolution. It is perceived to be a systematic creative process which ultimately improves the living standard of a society through the creation of new applications and products. The commercial paradigm that governs project selection, resource allocation and market penetration prevails when the focus shifts from pure research to applied research. Furthermore, the road to success through commercialisation is difficult for most inventors, especially in a vast and isolated country such as Australia which is located a long way from wealthy and developed economies.
While market leading products are considered unique, the actual process to achieve these products is essentially the same; progressing from an idea, through development to an outcome (if successful). Unfortunately, statistics indicate that only 3% of ‘ideas’ are significantly successful, 4% are moderately successful, and the remainder ‘evaporate’ in that form (Michael Quinn, Chairman, Innovation Capital Associates Pty Ltd).
This study demonstrates and analyses two techniques developed by the author which reduce uncertainty in the engineering design and development phase of new product development and therefore increase the probability of a successful outcome. This study expands the existing knowledge of the engineering design and development stage in the new product development process and is couched in the identification of practical methods, which have been successfully used to develop new products by Australian Small Medium Enterprise (SME) Excel Technology Group Pty Ltd (ETG).
Process theory is the term most commonly used to describe scientific study that identifies occurrences that result from a specified input state to an output state, thus detailing the process used to achieve an outcome. The thesis identifies relevant material and analyses recognised and established engineering processes utilised in developing new products. The literature identified that case studies are a particularly useful method for supporting problem-solving processes in settings where there are no clear answers or where problems are unstructured, as in New Product Development (NPD).
This study describes, defines, and demonstrates the process of new product development within the context of historical product development and a ‘live’ case study associated with an Australian Government START grant awarded to Excel Technology Group in 2004 to assist in the development of an image-based vehicle detection product. This study proposes two techniques which reduce uncertainty and thereby improve the probability of a successful outcome.
The first technique provides a predicted project development path or forward engineering plan which transforms the initial ‘fuzzy idea’ into a potential and achievable outcome. This process qualifies the ‘fuzzy idea’ as a potential, rationale or tangible outcome which is within the capability of the organisation. Additionally, this process proposes that a tangible or rationale idea can be deconstructed in reverse engineering process in order to create a forward engineering development plan. A detailed structured forward engineering plan reduces the uncertainty associated with new product development unknowns and therefore contributes to a successful outcome. This is described as the RETRO technique. The study recognises however that this claim requires qualification and proposes a second technique.
The second technique proposes that a two dimensional spatial representation which has productivity and consumed resources as its axes, provides an effective means to qualify progress and expediently identify variation from the predicted plan. This spatial representation technique allows a quick response which in itself has a prediction attribute associated with directing the project back onto its predicted path. This process involves a coterminous comparison between the predicted development path and the evolving actual project development path. A consequence of this process is verification of progress or the application of informed, timely and quantified corrective action. This process also identifies the degree of success achieved in the engineering design and development phase of new product development where success is defined as achieving a predicted outcome. This spatial representation technique is referred to as NPD Mapping. The study demonstrates that these are useful techniques which aid SMEs in achieving successful new product outcomes because the technique are easily administered, measure and represent relevant development process related elements and functions, and enable expedient quantified responsive action when the evolving path varies from the predicted path. These techniques go beyond time line representations as represented in GANTT charts and PERT analysis, and represent the base variables of consumed resource and productivity/technical achievement in a manner that facilitates higher level interpretation of time, effort, degree of difficulty, and product complexity in order to facilitate informed decision making. This study presents, describes, analyses and demonstrates an SME focused engineering development technique, developed by the author, that produces a successful new product outcome which begins with a ‘fuzzy idea’ in the mind of the inventor and concludes with a successful new product outcome that is delivered on time and within budget. Further research on a wider range of SME organisations undertaking new product development is recommended.
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Higgins, Paul Anthony. "Reducing uncertainty in new product development." Queensland University of Technology, 2008. http://eprints.qut.edu.au/20273/.
Full textAbstract:
Research and Development engineering is at the corner stone of humanity’s evolution. It is perceived to be a systematic creative process which ultimately improves the living standard of a society through the creation of new applications and products. The commercial paradigm that governs project selection, resource allocation and market penetration prevails when the focus shifts from pure research to applied research. Furthermore, the road to success through commercialisation is difficult for most inventors, especially in a vast and isolated country such as Australia which is located a long way from wealthy and developed economies.
While market leading products are considered unique, the actual process to achieve these products is essentially the same; progressing from an idea, through development to an outcome (if successful). Unfortunately, statistics indicate that only 3% of ‘ideas’ are significantly successful, 4% are moderately successful, and the remainder ‘evaporate’ in that form (Michael Quinn, Chairman, Innovation Capital Associates Pty Ltd).
This study demonstrates and analyses two techniques developed by the author which reduce uncertainty in the engineering design and development phase of new product development and therefore increase the probability of a successful outcome. This study expands the existing knowledge of the engineering design and development stage in the new product development process and is couched in the identification of practical methods, which have been successfully used to develop new products by Australian Small Medium Enterprise (SME) Excel Technology Group Pty Ltd (ETG).
Process theory is the term most commonly used to describe scientific study that identifies occurrences that result from a specified input state to an output state, thus detailing the process used to achieve an outcome. The thesis identifies relevant material and analyses recognised and established engineering processes utilised in developing new products. The literature identified that case studies are a particularly useful method for supporting problem-solving processes in settings where there are no clear answers or where problems are unstructured, as in New Product Development (NPD).
This study describes, defines, and demonstrates the process of new product development within the context of historical product development and a ‘live’ case study associated with an Australian Government START grant awarded to Excel Technology Group in 2004 to assist in the development of an image-based vehicle detection product. This study proposes two techniques which reduce uncertainty and thereby improve the probability of a successful outcome.
The first technique provides a predicted project development path or forward engineering plan which transforms the initial ‘fuzzy idea’ into a potential and achievable outcome. This process qualifies the ‘fuzzy idea’ as a potential, rationale or tangible outcome which is within the capability of the organisation. Additionally, this process proposes that a tangible or rationale idea can be deconstructed in reverse engineering process in order to create a forward engineering development plan. A detailed structured forward engineering plan reduces the uncertainty associated with new product development unknowns and therefore contributes to a successful outcome. This is described as the RETRO technique. The study recognises however that this claim requires qualification and proposes a second technique.
The second technique proposes that a two dimensional spatial representation which has productivity and consumed resources as its axes, provides an effective means to qualify progress and expediently identify variation from the predicted plan. This spatial representation technique allows a quick response which in itself has a prediction attribute associated with directing the project back onto its predicted path. This process involves a coterminous comparison between the predicted development path and the evolving actual project development path. A consequence of this process is verification of progress or the application of informed, timely and quantified corrective action. This process also identifies the degree of success achieved in the engineering design and development phase of new product development where success is defined as achieving a predicted outcome. This spatial representation technique is referred to as NPD Mapping. The study demonstrates that these are useful techniques which aid SMEs in achieving successful new product outcomes because the technique are easily administered, measure and represent relevant development process related elements and functions, and enable expedient quantified responsive action when the evolving path varies from the predicted path. These techniques go beyond time line representations as represented in GANTT charts and PERT analysis, and represent the base variables of consumed resource and productivity/technical achievement in a manner that facilitates higher level interpretation of time, effort, degree of difficulty, and product complexity in order to facilitate informed decision making. This study presents, describes, analyses and demonstrates an SME focused engineering development technique, developed by the author, that produces a successful new product outcome which begins with a ‘fuzzy idea’ in the mind of the inventor and concludes with a successful new product outcome that is delivered on time and within budget. Further research on a wider range of SME organisations undertaking new product development is recommended.
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Fadikar, Arindam. "Stochastic Computer Model Calibration and Uncertainty Quantification." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/91985.
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This dissertation presents novel methodologies in the field of stochastic computer model calibration and uncertainty quantification. Simulation models are widely used in studying physical systems, which are often represented by a set of mathematical equations. Inference on true physical system (unobserved or partially observed) is drawn based on the observations from corresponding computer simulation model. These computer models are calibrated based on limited ground truth observations in order produce realistic predictions and associated uncertainties. Stochastic computer model differs from traditional computer model in the sense that repeated execution results in different outcomes from a stochastic simulation. This additional uncertainty in the simulation model requires to be handled accordingly in any calibration set up.
Gaussian process (GP) emulator replaces the actual computer simulation when it is expensive to run and the budget is limited. However, traditional GP interpolator models the mean and/or variance of the simulation output as function of input. For a simulation where marginal gaussianity assumption is not appropriate, it does not suffice to emulate only the mean and/or variance. We present two different approaches addressing the non-gaussianity behavior of an emulator, by (1) incorporating quantile regression in GP for multivariate output, (2) approximating using finite mixture of gaussians. These emulators are also used to calibrate and make forward predictions in the context of an Agent Based disease model which models the Ebola epidemic outbreak in 2014 in West Africa.
The third approach employs a sequential scheme which periodically updates the uncertainty inn the computer model input as data becomes available in an online fashion. Unlike other two methods which use an emulator in place of the actual simulation, the sequential approach relies on repeated run of the actual, potentially expensive simulation.Doctor of Philosophy
Mathematical models are versatile and often provide accurate description of physical events. Scientific models are used to study such events in order to gain understanding of the true underlying system. These models are often complex in nature and requires advance algorithms to solve their governing equations. Outputs from these models depend on external information (also called model input) supplied by the user. Model inputs may or may not have a physical meaning, and can sometimes be only specific to the scientific model. More often than not, optimal values of these inputs are unknown and need to be estimated from few actual observations. This process is known as inverse problem, i.e. inferring the input from the output. The inverse problem becomes challenging when the mathematical model is stochastic in nature, i.e., multiple execution of the model result in different outcome. In this dissertation, three methodologies are proposed that talk about the calibration and prediction of a stochastic disease simulation model which simulates contagion of an infectious disease through human-human contact. The motivating examples are taken from the Ebola epidemic in West Africa in 2014 and seasonal flu in New York City in USA.
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Vassiliadis, Constantine. "Integration of maintenance optimization in process design and operation under uncertainty." Thesis, Imperial College London, 2000. http://hdl.handle.net/10044/1/8590.
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Saraidaris, C. "The optimal design of chemical processes considering multiple objectives and uncertainty." Thesis, University of Manchester, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.384384.
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Péron, Martin Brice. "Optimal sequential decision-making under uncertainty." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/120831/1/Martin%20Brice_Peron_Thesis.pdf.
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This thesis develops novel mathematical models to make optimal sequential decisions under uncertainty. One of the main objectives is to scale Markov decision processes, the framework of choice for selecting the best sequential decisions, to larger problems. The thesis is motivated by the management of the invasive tiger mosquito Aedes albopictus across the Torres Strait Islands, an archipelago of islands at the doorstep of the Australian mainland.
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Samuelsson, Jonathan, and Lovisa Skoglund. "Uncertainty in process innovations : A case study on the adaption of search engine optimization." Thesis, Internationella Handelshögskolan, Jönköping University, IHH, Företagsekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48574.
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Process innovation is an important topic in business research. It enables competitive advantages for companies if managed properly. It is previously acknowledged that uncertainty in process innovation is common and previously research show that it has a negative impact on process innovation projects, as it can cause a waste of resources for the company. For SME’s, where resources are limited, it is imperative that uncertainty do not affect process innovation projects negatively. Previous scholars do not identify sources of process innovation uncertainty in SME’s or how it can be managed, thus leave a gap in theory that is important to fill. The purpose of the study was to investigate how uncertainty in process innovation arises in an SME and how it can be reduced by an investigation on how SEO, as an instance of process innovation, was perceived before and after an implementation process and if a change in perception was related to uncertainty. A single case study with qualitative interviews, combined with an implementation process of SEO, was used to investigate the topic and generate in-depth knowledge. Our findings identify sources of process innovation uncertainty in SME’s, arising from either a resource perspective or from an organizational perspective. Furthermore, we suggest how to manage the identified sources of uncertainty through either information, communication or results. Organizations can use these findings to manage process innovation uncertainty before it arises, thus achieve successful process innovation.
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Sabio, Arteaga Nagore. "Contribution to the development of more sustainable process industries under uncertainty." Doctoral thesis, Universitat Rovira i Virgili, 2016. http://hdl.handle.net/10803/457188.
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En les últimes dècades, els reptes originats com a resultat dels elevats preus de l'energia i la creixent pressió per reduir les emissions de gasos d'efecte hivernacle han estimulat un gran interès en la investigació relacionada amb sistemes energètics i de processos. D'una banda, les indústries de procés s'enfronten a la necessitat de cobrir la creixent demanda energètica en un mercat afectat cada vegada per més incertesa. De l'altra, els recursos utilitzats tradicionalment com a suport per al desenvolupament comencen a mostrar impactes ambientals que podrien posar en perill el desenvolupament sostenible de les espècies. En conseqüència, la situació actual es pot descriure com guiada al voltant de tres eixos principals: energia, sostenibilitat i incertesa. De vital importància per a aquests problemes és la recerca en tecnologia de sistemes assistida per ordinador, per al desenvolupament d'estratègies que investiguin l'impacte de les indústries de procés en tots dos, l'eficiència del sistema i el seu impacte ambiental de cicle de vida en presència d'incertesa . En aquest sentit, l'objectiu general d'aquesta tesi és dirigir aquests reptes primer realitzant un pas endavant en l'acostament entre els marges de la investigació científica i la investigació de sistemes en l'àrea d'enginyeria de sistemes de processos. El problema s'enfoca ideant un conjunt d'eines avançades de programació matemàtica multi-objectiu capaços de tractar amb la problemàtica ambiental i d'incertesa en el disseny i planificació d'indústries de procés més sostenibles. Això es porta a terme afegint múltiples mètriques estocàstiques i de cicle de vida, i aplicant anàlisi de components principals per identificar mètriques redundants. Els models presentats, són llavors capaços de tractar sistemes d'una única o de múltiples plantes de procés, i d'atendre de manera holística les tres majors fonts d'incertesa: paramètrica, estructural i metodològica.En las últimas décadas, los retos originados como resultado de los elevados precios de la energía y la creciente presión por reducir las emisiones de gases de efecto invernadero han estimulado un gran interés en la investigación relacionada con sistemas energéticos y de procesos. Por un lado, las industrias de proceso se enfrentan a la necesidad de cubrir la creciente demanda energética en un mercado afectado cada vez por más incertidumbre. Por otro, los recursos utilizados tradicionalmente como soporte para el desarrollo comienzan a mostrar impactos ambientales que podrían poner en peligro el desarrollo sostenible de las especies. En consecuencia, la situación actual se puede describir como guiada alrededor de tres ejes principales: energía, sostenibilidad e incertidumbre. De vital importancia para estos problemas es la investigación en tecnología de sistemas asistida por ordenador, para el desarrollo de estrategias que investiguen el impacto de las industrias de proceso en ambos, la eficiencia del sistema y su impacto ambiental de ciclo de vida en presencia de incertidumbre. En este sentido, el objetivo general de esta tesis es dirigir estos retos primero realizando un paso adelante en el acercamiento entre los márgenes de la investigación científica y la investigación de sistemas en el área de ingeniería de sistemas de procesos. El problema se enfoca ideando un conjunto de herramientas avanzadas de programación matemática multi-objectivo capaces de tratar con la problemática ambiental y de incertidumbre en el diseño y planificación de industrias de proceso más sostenibles. Esto se lleva a cabo añadiendo múltiples métricas estocásticas y de ciclo de vida, y aplicando análisis de componentes principales para identificar métricas redundantes. Los modelos presentados, son entonces capaces de tratar sistemas de una única o de múltiples plantas de proceso, y de atender de manera holística las tres mayores fuentes de incertidumbre: paramétrica, estructural y metodológica.
Over the past decades, the challenges originated as a result of high energy prices and the growing pressure to reduce greenhouse gas emissions have fuelled a large interest in energy and process systems related research. On the one hand, process industries are faced with the need to cover the increasing demand for energy as developing nations grow and developed countries continue to progress in an increasingly uncertain marketplace, and on the other hand, the resources that have traditionally supported this continued progress begin to show environmental impacts that could threaten the sustainable development of species in the world. As a consequence, the present situation could be described as driven along three main edges: energy, sustainability and uncertainty. Of particular relevance for these problems is research on computer-aided systems technology to develop strategies for investigating the impact of process industries on both, the system efficiency and its life cycle environmental impact in the presence of uncertainty. In this sense, the general goal of this thesis is to explicitly address these challenges by first making a step towards closing the gap between science-based and systems-based research in Process Systems Engineering. The problem is addressed by devising a set of advanced multi-objective mathematical programming tools able to deal with environmental and uncertainty concerns in the design and planning of more sustainable process industries. In particular, multiple life cycle assessment and risk management stochastic metrics are appended to the optimization MILP and MINLP problems as additional criteria to be optimized, and Principal Components Analysis is applied for identifying redundant life cycle metrics and reduce the problem dimensionality. These models presented here are thus able to deal with single-site and multi-site process systems are capable of addressing, in a holistic manner, the three major sources of uncertainty: parameter, model and methodological.
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Numminen, Emil. "Software Investments under Uncertainty : Modeling Intangible Consequences as a Stochastic Process." Licentiate thesis, Karlskrona : School of Management, Blekinge Institute of Technology, 2008. http://www.bth.se/fou/Forskinfo.nsf/allfirst2/c4fc1a96b53c2937c125746500360fec?OpenDocument.
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Xu, Zhiyu 1973. "Two approaches to buffer management under demand uncertainty : an analytical process." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28520.
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Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2004.Includes bibliographical references (leaves 66-67).
(cont.) boundary and leave more demand uncertainty to the pull part of the system.
Based on a particular case study, this paper presents two approaches to buffer management under demand uncertainty, which is characterized by high lumpiness, dispersion and volatility. The common theme of both of the two approaches is not to find an advanced statistical method to improve demand forecast on the basis of historical data. Rather, these approaches provide new business paradigms to deal with demand uncertainty. The first approach, make-to-anticipated-order (MTAO), takes advantage of the mechanism of make-to-order (MTO) and develops a process that the production is pulled by anticipated orders instead of being pushed by the forecast of unpredictable future demand. The implementation of this method, on one hand, breaks through the precondition of MTO that the total production cycle time should be less than customers' desired lead-time. On the other hand, MTAO enjoys the advantage of arranging production by responding to customer demand to reduce inventory costs and obsolescence risks of MPS level items. The second approach makes use of postponement and commonality strategy to lower demand uncertainty. The basic principle is that aggregate demand is more stable than disaggregate demand. Thus, if a common module instead of various individual modules in a module family acts as a MPS item, the demand of the common module will represent the aggregate demand of all individual modules in the module family and more accurate forecast can be made. Then by using the forecasted demand distribution of the common module, we can figure out optimized multistage inventory placement to buffer demand uncertainty with the minimum holding cost of total safety stock. In effect, by implementing postponement and commonality strategy, we change the push-pull
by Zhiyu Xu.
M.Eng.in Logistics
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Tavares, Ivo Alberto Valente. "Uncertainty quantification with a Gaussian Process Prior : an example from macroeconomics." Doctoral thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/21444.
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Doutoramento em Matemática Aplicada à Economia e GestãoThis thesis may be broadly divided into 4 parts. In the first part, we do a literature review of the state of the art in misspecification in Macroeconomics, and what so far has been the contribution of a relatively new area of research called Uncertainty Quantification to the Macroeconomics subject. These reviews are essential to contextualize the contribution of this thesis in the furthering of research dedicated to correcting non-linear misspecifications, and to account for several other sources of uncertainty, when modelling from an economic perspective. In the next three parts, we give an example, using the same simple DSGE model from macroeconomic theory, of how researchers may quantify uncertainty in a State-Space Model using a discrepancy term with a Gaussian Process prior. The second part of the thesis, we used a full Gaussian Process (GP) prior on the discrepancy term. Our experiments showed that despite the heavy computational constraints of our full GP method, we still managed to obtain a very interesting forecasting performance with such a restricted sample size, when compared with similar uncorrected DSGE models, or corrected DSGE models using state of the art methods for time series, such as imposing a VAR on the observation error of the state-space model. In the third part of our work, we improved on the computational performance of our previous method, using what has been referred in the literature as Hilbert Reduced Rank GP. This method has close links to Functional Analysis, and the Spectral Theorem for Normal Operators, and Partial Differential Equations. It indeed improved the computational processing time, albeit just slightly, and was accompanied with a similarly slight decrease in the forecasting performance. The fourth part of our work delved into how our method would account for model uncertainty just prior, and during, the great financial crisis of 2007-2009. Our technique allowed us to capture the crisis, albeit at a reduced applicability possibly due to computational constraints. This latter part also was used to deepen the understanding of our model uncertainty quantification technique with a GP. Identifiability issues were also studied. One of our overall conclusions was that more research is needed until this uncertainty quantification technique may be used in as part of the toolbox of central bankers and researchers for forecasting economic fluctuations, specially regarding the computational performance of either method.
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Jederström, Kathrina, and Sebastian Andersson. "Process Innovation Challenges : - how to reduce Uncertainty through Discrete Event Simulation." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-35731.
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In today’s competitive market, a company will not succeed unless they stand out in other ways than pure benefits with its products. This can be reached by in some way altering the process currently in place. One way, is by introducing process innovation. Advantages related to the adoption of process innovation has been found in literature, for example by increase competitiveness, increase productivity, and increase plant visibility. However, process innovation evokes uncertainty. Discrete Event Simulation (DES) models has in previous research been suggested as a tool to reduce uncertainty at manufacturing companies when they are undergoing changes. However, the study of change in process innovation setting has been largely ignored. By acknowledging this gap in current research, the aim of this study is to investigate whether the use of DES models are able to reduce uncertainties in process innovation. The study is guided by three research questions: 1. What are the characteristics of process innovation introduction in a production process context? 2. How is the production process at manufacturing companies affected by process innovation related uncertainties? 3. How can the usage of DES contribute to reduction of uncertainties during the introduction of process innovation at manufacturing companies? In order to answer these questions, a research methodology consisting of a literature review and a case study including the usage of DES were applied. In this thesis the case study is conducted at a manufacturing company, presented with the goal of making a modification in the production. This is done in an attempt to make it more environmental friendly while also establish a competitive edge over the rivals. To reach this, an implementation of a process innovation technology is under planning, but introducing something new creates numerous uncertainties. To be able to implement this process innovation, uncertainty reduction is crucial. By identify literature within the field, and compare with findings from interviews and workshops at the studied company, process innovation characteristics and how process innovation evokes uncertainties were identified. From the usage DES in this thesis, uncertainties were reduced, partly reduced and identified while some uncertainties remain unresolved. Moreover, the findings point to the creation of the simulation model working as a visualisation of the current production and the possible future, which generates a discussion platform for all stakeholders involved.I den nuvarande konkurrenskraftiga marknaden har ett företag många utmaningar ifall de vill lyckas. Det räcker nämligen inte längre med att ha bra produkter utan de måste också förbättras på andra sätt. Ett sätt att uppnå detta på, är att genomföra förändringar i den nuvarande produktionen, en metod för detta är introducera en processinnovation på företaget. Under detta arbete har fördelarna relaterade till processinnovation upptäckts i befintlig litteratur, till exempel genom att ökad konkurrentskraftighet, produktivitet och synlighet för fabriken. Dessvärre framkallar implementeringen av en processinnovation osäkerheter. Diskret händelse simulering (DES) modeller har i tidigare forskning föreslagits som ett verktyg för at minska osäkerheter i tillverkningsföretag, medan de planerar att genomgår en förändring. Forskning om hur simulering hanterar fabriker som genomgår en processinnovation har i hög grad ignorerats. De här studien har för avsikt att undersöka just det området där nuvarande forskning brister, nämligen om ifall DES modeller kan minska osäkerheter i processinnovationer. Tre forskningsfrågor har tagits fram för att styra arbetet: 1. Vilka kännetecken har introduktionen av processinnovation i en produktionsprocess kontext? 2. Hur påverkas produktionsprocess hos tillverkande företag av de osäkerheter som processinnovation medför? 3. Hur kan DES användas för att bidra till minskandet av osäkerheter i tillverkande företag som introducerar processinnovation? För att besvara dessa frågor genomfördes an litteraturstudie och en fallstudie som innehöll simulering. Fallstudien som utfördes på ett tillverkningsföretag som är i planeringsstadiet för att införa en processinnovation. Innovationen har för avsikt att göra produktionen mer miljövänlig och samtidigt skapa en fördel över konkurrenterna. Nuvarande planering är fylld av osäkerheter eftersom tillägget av någonting nytt alltid gör det. Därför är reduceringen av osäkerheter avgörande för att en implementering ska kunna genomföras Genom att identifiera forskning inom området, och jämföra den med resultat från företagsrelaterade intervjuer och workshops, identifierade kännetecken på processinnovation och hur processinnovation skapar osäkerheter. Genom att använda DES i examensarbetet, minskades antalet osäkerheter, till fullo och delvis, och nya osäkerheter identifierades. Dessutom visar resultat på studien att simulering kan användas som ett visualiseringsverktyg för att skapa en diskussionsplattform angående framtida förändringar i produktionen.
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Rebolledo, Wueffer Mario. "Situation based process monitoring in complex systems considering vagueness and uncertainty." [S.l. : s.n.], 2004. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11814255.
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Zepeda, Ariel. "Cultivating Uncertainty Through a Multimodal Perspective on Process to Encourage Transfer." CSUSB ScholarWorks, 2018. https://scholarworks.lib.csusb.edu/etd/765.
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This thesis considers the ways in which a multimodal approach to teaching writing process can help students better understand the choices available as they navigate first-year writing and beyond. Such an approach destabilizes their understanding of what counts as writing, beyond the strictly text-based practices they may normally associate with writing. This destabilization emphasizes the uncertainty of writing as a productive frame of mind, as it encourages a more critical approach for students as they develop and adapt their writing processes. A multimodal perspective on writing process encourages a more proactive approach to students’ development of a repertoire of writing knowledge and practice to increase their chances of transfer.
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Wijesiri, Buddhi. "Assessing uncertainty in relation to urban stormwater pollutant processes." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/96018/5/Buddhi%20Wijesiri%20Thesis.pdf.
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This research study created new knowledge relating to urban stormwater pollutant process variability, and thereby developed an innovative approach to quantitatively assess the uncertainty associated with stormwater quality modelling outcomes. Build-up and wash-off of particulate solids of different size ranges and particle-bound heavy metals from urban road surfaces were analysed with the aid of mathematical replication models of these processes. The outcomes of this research study will contribute to enhanced planning and management decisions in relation to designing urban stormwater pollution mitigation strategies.document.getElementsByName("c12_disable_contact")[0].checked = true;
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Macatula, Romcholo Yulo. "Linear Parameter Uncertainty Quantification using Surrogate Gaussian Processes." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99411.
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We consider uncertainty quantification using surrogate Gaussian processes. We take a previous sampling algorithm and provide a closed form expression of the resulting posterior distribution. We extend the method to weighted least squares and a Bayesian approach both with closed form expressions of the resulting posterior distributions. We test methods on 1D deconvolution and 2D tomography. Our new methods improve on the previous algorithm, however fall short in some aspects to a typical Bayesian inference method.Master of Science
Parameter uncertainty quantification seeks to determine both estimates and uncertainty regarding estimates of model parameters. Example of model parameters can include physical properties such as density, growth rates, or even deblurred images. Previous work has shown that replacing data with a surrogate model can provide promising estimates with low uncertainty. We extend the previous methods in the specific field of linear models. Theoretical results are tested on simulated computed tomography problems.
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Scudieri, Paul Anthony. "A Constraint Based Model of the Design Process: Complexity, Uncertainty, and Change." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1376579182.
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Olson, Rickard, Erik Forsman, and Tommy Brehmer. "The Investment Process : Risk and Uncertainty Handling in Small and Medium Sized Subcontractors." Thesis, Jönköping University, JIBS, Business Administration, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-139.
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Guergachi, Abdelaziz. "Uncertainty management in the activated sludge process, innovative applications of computational learning theory." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0016/NQ58278.pdf.
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Skinner, Laura. "Negotiating uncertainty : mental health professionals’ experiences of the Mental Health Act assessment process." Thesis, University of Leicester, 2006. http://hdl.handle.net/2381/8972.
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PIZZO, MIRIAM DA SILVA. "IMPACT OF ORGANIZATIONAL ENVIRONMENT UNCERTAINTY IN THE PLANNING PROCESS: THE CASE OF VARIG." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2003. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=3625@1.
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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIORNeste trabalho pretende-se mostrar a relevância da realização do planejamento, mesmo em situações complexas que envolvam os mais diversos fatores internos e externos à organização, e a importância de saber a melhor forma de tomada de decisão de acordo com cada circunstância. Visando compreender os elementos estratégicos de uma empresa inserida em um mercado altamente dinâmico, desenvolveu-se um arcabouço teórico tratando de planejamento em condições de incerteza e análise do ambiente organizacional. Tendo como base esses elementos, elaborou-se uma avaliação do setor de aviação comercial e uma análise estratégica companhia aérea VARIG Brasil. Os resultados dessa avaliação indicaram as deficiências do posicionamento estratégico dessa metodologia dos processos decisórios para que se possa obter melhor desempenho.
The objective of this dissertation is to show the relevance of planning even in complex situations, involving the most diverse factors, internal or external to the organization, as well as the importance of recognizing the best alternatives in decision making, according to each circumstance. Willing to understand the strategic elements of an organization inserted in a highly dynamic market, a theoretical basis has been developed dealing with planning under uncertainty and organizational environment analysis. With such basic elements, an evaluation of the commercial aviation business and a strategic analysis of VARIG Brasil airline were elaborated. The results of this evaluation indicated the deficiencies of the strategic position of the Company and pointed out the need of a revaluating and improving the decision process in order to attain better performance.
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Fugleberg, Eric N. (Eric Nels). "Uncertainty quantification and calibration in nuclear safety codes using Gaussian process active learning." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106691.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 85-87).
Inverse problems and inverse uncertainty quantification (UQ) are challenging issues when dealing with complex and highly non-linear functions. Methods have been developed to decrease the computational burden by using the Gaussian Process (GP) emulator model framework to approximate the input-output relation of a deterministic computer code. The GP emulator can then be used in place of the computer code to perform Bayesian calibration techniques to determine uncertain parameter distribution. The performance of a GP emulator is largely dependent on the quality of the points in its training set; the best emulator exactly replicates the output of the computer code. The uncertain parameter posterior sample space is not known a priori, resulting in GP training sets covering as much of the prior sample space as possible in hopes of covering the posterior space well enough. This work improves the performance of the simple GP emulator using an active learning methodology to select additional training points which cover the posterior sample space of the unknown parameters. Furthermore, the effect of the covariance function on the performance of the GP is investigated with recommendations made for future GP emulator applications.
by Eric Nels Fugleberg.
S.M.
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Webb, Michael John. "Estimating Uncertainty Attributable to Inconsistent Pairwise Comparisons in the Analytic Hierarchy Process (AHP)." Thesis, The George Washington University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10751947.
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This praxis explores a new approach to the problem of estimating the uncertainty attributable to inconsistent pairwise comparison judgments in the Analytic Hierarchy Process (AHP), a prominent decision-making methodology used in numerous fields, including systems engineering and engineering management. Based on insights from measurement theory and established error propagation equations, the work develops techniques to estimate the uncertainty of aggregated priorities for decision alternatives based on measures of inconsistency for component pairwise comparison matrices. This research develops two formulations for estimating the error: the first, more computationally intensive and accurate, uses detailed calculations of parameter errors to estimate the aggregated uncertainty, while the second, significantly simpler, uses an estimate of mean relative error (MRE) for each pairwise comparison matrix to estimate the aggregated error. This paper describes the derivation of both formulations for the linear weighted sum method of priority aggregation in AHP and uses Monte Carlo simulation to test their estimation accuracies for diverse problem structures and parameter values. The work focuses on the two most commonly used methods of deriving priority weights in AHP: the eigenvector method (EVM) and the geometric mean method (GMM). However, the approach of estimating the propagation of measurement errors can be readily applied to other hierarchical decision support methodologies that use pairwise comparison matrices. The developed techniques provide analysts the ability to easily assess decision model uncertainties attributable to comparative judgment inconsistencies without recourse to more complex optimization routines or simulation experiments described previously in the professional literature.
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Bartlett, Elizabeth Kay. "Evaluating the design process of a four-bar-slider mechanism using uncertainty techniques." Master's thesis, Mississippi State : Mississippi State University, 2002. http://library.msstate.edu/etd/show.asp?etd=etd-04092002-180523.
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Darira, Rishi. "Modeling demand uncertainty and processing time variability for multi-product chemical batch process." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000401.
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Rebolledo, Mario [Verfasser]. "Situation-based process monitoring in complex systems considering vagueness and uncertainty / Mario Rebolledo." Aachen : Shaker, 2005. http://d-nb.info/975034162/34.
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Lee, Hyun Cheol. "Robust design of control charts for autocorrelated processes with model uncertainty." Texas A&M University, 2004. http://hdl.handle.net/1969.1/2778.
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Statistical process control (SPC) procedures suitable for autocorrelated processes have been extensively investigated in recent years. The most popular method is the residual-based control chart. To implement this method, a time series model, which is usually an autoregressive moving average (ARMA) model, of the process is required. However, the model must be estimated from data in practice and the resulting ARMA modeling errors are unavoidable. Residual-based control charts are known to be sensitive to ARMA modeling errors and often suffer from inflated false alarm rates. As an alternative, control charts can be applied directly to the autocorrelated data with widened control limits. The widened amount is determined by the autocorrelation function of the process. The alternative method, however, can not be also free from the effects of modeling errors because it relies on an accurate process model to be effective.
To compare robustness to the ARMA modeling errors between the preceding two kinds of methods for control charting autocorrelated data, this dissertation investigates the sensitivity analytically. Then, two robust design procedures for residual-based control charts are developed from the result of the sensitivity analysis. The first approach for robust design uses the worst-case (maximum) variance of a chart statistic to guarantee the initial specification of control charts. The second robust design method uses the expected variance of the chart statistic. The resulting control limits are widened by an amount that depends on the variance of chart statistic - maximum or expected - as a function of (among other things) the parameter estimation error covariances.
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Calfa, Bruno Abreu. "Data Analytics Methods for Enterprise-wide Optimization Under Uncertainty." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/575.
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This dissertation primarily proposes data-driven methods to handle uncertainty in problems related to Enterprise-wide Optimization (EWO). Datadriven methods are characterized by the direct use of data (historical and/or forecast) in the construction of models for the uncertain parameters that naturally arise from real-world applications. Such uncertainty models are then incorporated into the optimization model describing the operations of an enterprise. Before addressing uncertainty in EWO problems, Chapter 2 deals with the integration of deterministic planning and scheduling operations of a network of batch plants. The main contributions of this chapter include the modeling of sequence-dependent changeovers across time periods for a unitspecific general precedence scheduling formulation, the hybrid decomposition scheme using Bilevel and Temporal Lagrangean Decomposition approaches, and the solution of subproblems in parallel. Chapters 3 to 6 propose different data analytics techniques to account for stochasticity in EWO problems. Chapter 3 deals with scenario generation via statistical property matching in the context of stochastic programming. A distribution matching problem is proposed that addresses the under-specification shortcoming of the originally proposed moment matching method. Chapter 4 deals with data-driven individual and joint chance constraints with right-hand side uncertainty. The distributions are estimated with kernel smoothing and are considered to be in a confidence set, which is also considered to contain the true, unknown distributions. The chapter proposes the calculation of the size of the confidence set based on the standard errors estimated from the smoothing process. Chapter 5 proposes the use of quantile regression to model production variability in the context of Sales & Operations Planning. The approach relies on available historical data of actual vs. planned production rates from which the deviation from plan is defined and considered a random variable. Chapter 6 addresses the combined optimal procurement contract selection and pricing problems. Different price-response models, linear and nonlinear, are considered in the latter problem. Results show that setting selling prices in the presence of uncertainty leads to the use of different purchasing contracts.