The occurrence of catastrophic incidents in the process industry leave a marked legacy of resulting in staggering economic and societal losses incurred by the company, the government and the society. The work described herein is a novel approach proposed to help predict and mitigate potential catastrophes from occurring and for understanding the stakes at risk for better risk informed decision making. The methodology includes societal impact as risk measures along with tangible asset damage monetization. Predicting incidents as leading metrics is pivotal to improving plant processes and, for individual and societal safety in the vicinity of the plant (portfolio). From this study it can be concluded that the comprehensive judgments of all the risks and losses should entail the analysis of the overall results of all possible incident scenarios. Value-at-Risk (VaR) is most suitable as an overall measure for many scenarios and for large number of portfolio assets. FN-curves and F$-curves can be correlated and this is very beneficial for understanding the trends of historical incidents in the U.S. chemical process industry. Analyzing historical databases can provide valuable information on the incident occurrences and their consequences as lagging metrics (or lagging indicators) for the mitigation of the portfolio risks. From this study it can be concluded that there is a strong statistical relationship between the different consequence tiers of the safety pyramid and Heinrich‘s safety pyramid is comparable to data mined from the HSEES database. Furthermore, any chemical plant operation is robust only when a strategic balance is struck between optimal plant operations and, maintaining health, safety and sustaining environment. The balance emerges from choosing the best option amidst several conflicting parameters. Strategies for normative decision making should be utilized for making choices under uncertainty. Hence, decision theory is utilized here for laying the framework for choice making of optimum portfolio option among several competing portfolios. For understanding the strategic interactions of the different contributing representative sets that play a key role in determining the most preferred action for optimum production and safety, the concepts of game theory are utilized and framework has been provided as novel application to chemical process industry.

This thesis aims to predict the probable undiscovered geothermal systems through investigation of spatial relation between geothermal occurrences and its surrounding geological phenomenon in Western Anatolia. In this context, four different public data, which are epicenter map, lineament map, Bouger gravity anomaly and magnetic anomaly maps, are utilized. In order to extract the necessary information for each map layer the raw public data is converted to a synthetic data which are directly used in the analysis. Synthetic data employed during the investigation process include Gutenberg-Richter b-value map, distance to lineaments map and distance to major grabens present in the area. Thus, these three layers including directly used magnetic anomaly maps are combined by means of Boolean logic model and Weights of Evidence method (WofE), which are multicriteria decision methods, in a Geographical Information System (GIS) environment. Boolean logic model is based on the simple logic of Boolean operators, while the WofE model depends on the Bayesian probability. Both of the methods use binary maps for their analysis. Thus, the binary map classification is the key point of the analysis. In this study three different binary map classification techniques are applied and thus three output maps were obtained for each of the method. The all resultant maps are evaluated within and among the methods by means of success indices. The findings reveal that the WofE method is better predictor than the Boolean logic model and that the third binarization approach, which is named as optimization procedure in this study, is the best estimator of binary classes due to obtained success indices. Finally, three output maps of each method are combined and the favorable areas in terms of geothermal potential are produced. According to the final maps the potential sites appear to be Aydin, Denizli and Manisa, of which first two have been greatly explored and exploited since today and thus not surprisingly found as potential in the output maps, while Manisa when compared to first two is nearly virgin.

Our country is a risky position in terms of natural disasters. In the long run, preferentially settlement areas were selected to ensure maximum benefits in terms of both economic and security aspects, other criteria is not taken account when selection of sites. The main purpose of this study is to examine and compare the properties of settlement location based on natural hazard and environmental constraints to be able to understand the interaction between the settlements and natural conditions at the regional scale of YeSilirmak Basin. A MCDA was set up with 10 different data layers in two data domains (environmental and natural hazards domains), are evaluated. The results of the MCDA scores are then transferred to settlement databases in order to evaluate the number of existing settlements in different environmental and natural hazard related suitability classes. It is found that almost 29% of YeSilirmak catchment is environmentally favorable for settlement, and in coherence with that 41% of all existing settlements are located in this zone, indicating a clear preference among the perception of environmentally better places to be settled in. On the other hand with respect to the natural hazards dataset, the locations of the settlements fail to create any preference, as 73,32% of the area is used by 73,50% of existing settlements, which indicates that the perception of natural hazards are low and do not effect settlement criteria, while the acceptable risk of community is high.

New Zealand faces a range of food safety hazards. Microbial hazards alone were estimated to cause over 2,000 years of lost healthy life in 2011 (Cressey, 2012) and $62m in medical costs and lost productivity in 2009 (Gadiel & Abelson, 2010). Chemical hazards are thought to be well managed through existing controls (Vannoort & Thomson, 2009) whereas microbial hazards are considered harder to control, primarily due to their ability to reproduce along the food production chain. Microbial hazards are thought to cause the majority of acute foodborne gastroenteritis. This research reviewed food safety literature and official documentation, and conducted 55 interviews, mostly with food safety experts from different stakeholder groups, to examine the food safety decision-making environment in New Zealand. This research explores the concept of the ‘stakeholder’ in the context of food safety decision-making and proposes an inclusive ‘stakeholder’ definition as any group which is able to affect, or be affected by, the decision-making process. Utilising this definition, and guided by interviews, New Zealand stakeholders in food safety decision-making were identified and classified as follows: •Regulators •Public health authorities •Food safety scientists/academics •Consumers •Māori •Food Businesses (further classified as): o Farmers o Processors o Food retailers o Exporters Interviews with stakeholders from these groups highlighted twelve criteria as being relevant to multiple groups during food safety intervention evaluation: •Effectiveness •Financial cost •Market Access •Consumer Perceptions •Ease of Implementation •Quality or Suitability •Quality of Science •Equity of Costs •Equity of Benefits •Workplace Safety •Cultural Impact •Animal Welfare There are a number of different ways to measure or assess performance on these criteria. Some are able to be quantitatively measured, while others may require the use of value judgements. This thesis used the Disability-Adjusted Life Year (DALY) metric for quantifying effectiveness during the testing of different MCDA models. This thesis reviews the MCDA process and the food safety specific MCDA literature. There are different ways of conducting MCDA. In particular, there are a large number of models available for the aggregation phase; the process of converting model inputs, in the form of criteria scores and weights, into model recommendations. This thesis has described and reviewed the main classes of model. The literature review and interview process guided the construction and testing of three classes of MCDA model; the Weighted Sum, Analytic Hierarchy Process (AHP) and PROMETHEE models. These models were selected due to their having different characteristics and degrees of complexity, as well as their popularity in the food safety and Health Technology Assessment (HTA) literature. Models were tested on the problem of selecting the most appropriate intervention to address the historic Campylobacter in poultry problem in New Zealand during the mid-2000s. Experimentation was conducted on these models to explore how different configurations utilise data and produce model outputs. This experimentation included: •Varying the format of input data •Exploring the effects of including/excluding criteria •Methods for sensitivity analysis •Exploring how data inputs and outputs can be elicited and presented using visual tools • Creating and using hybrid MCDA models The results of this testing are a key output of this thesis and provide insight into how such models might be used in food safety decision-making. The conclusions reached throughout this research phase can be classified into one of two broad groups: •Those relating to MCDA as a holistic process/methodology for decision-making •Those relating to the specific models and mathematical procedures for generating numerical inputs and outputs This thesis demonstrates that food-safety decision-making is a true multi-criteria, multi-stakeholder problem. The different stakeholders in food-safety decision-making do not always agree on the value and importance of the attributes used to evaluate competing intervention schemes. MCDA is well suited to cope with such complexity as it provides a structured methodology for the systematic and explicit identification, recording and aggregation of qualitative and quantitative information, gathered from a number of different sources, with the output able to serve as a basis for decision-making. The MCDA models studied in this thesis range from models that are simple and quick to construct and use, to more time consuming models with sophisticated algorithms. The type of model used for MCDA, the way these models are configured and the way inputs are generated or elicited can have a significant impact on the results of an analysis. This thesis has identified a number of key methodological considerations for those looking to employ one of the many available MCDA models. These considerations include: •Whether a model can accommodate the type and format of input data •The desired degree of compensation between criteria (i.e. full, partial or no compensation) •Whether the goal of an analysis is the identification of a ‘best’ option(s), or the facilitation of discussion, and communication of data •The degree of transparency required from a model and whether an easily understood audit trail is desired/required •The desired output of a model (e.g. complete or partial ranking). This thesis has also identified a number of practical considerations when selecting which model to use in food safety decision-making. These include: •The amount of time and energy required of stakeholders in the generation of data inputs (elicitation burden) •The degree of training required for participants •How data inputs are to be elicited and aggregated in different group decision-making environments •The availability of MCDA software for assisting an analysis Considering the above points will assist users in selecting a suitable MCDA model that meets their requirements and constraints. This thesis provides original and practical knowledge to assist groups or individuals looking to employ MCDA in the context of food-safety intervention decision-making. This research could also serve as a guide for those looking to evaluate a different selection of MCDA models.

Many important decisions must be made without full information. For example, a woman may need to make a treatment decision regarding breast cancer without full knowledge of important uncertainties, such as how well she might respond to treatment. In the financial domain, in the wake of the housing crisis, the government may need to monitor the credit market and decide whether to intervene. A key input in this case would be a model to describe the chance that one person (or company) will default given that others have defaulted. However, such a model requires addressing the lack of knowledge regarding the correlation between groups or individuals. How to model and make decisions in cases where only partial information is available is a significant challenge. In the past, researchers have made arbitrary assumptions regarding the missing information. In this research, we developed a modeling procedure that can be used to analyze many possible scenarios subject to strict conditions. Specifically, we developed a new Monte Carlo simulation procedure to create a collection of joint probability distributions, all of which match whatever information we have. Using this collection of distributions, we analyzed the accuracy of different approximations such as maximum entropy or copula-models. In addition, we proposed several new approximations that outperform previous methods. The objective of this research is four-fold. First, provide a new framework for approximation models. In particular, we presented four new models to approximate joint probability distributions based on geometric attributes and compared their performance to existing methods. Second, develop a new joint distribution simulation procedure (JDSIM) to sample joint distributions from the set of all possible distributions that match available information. This procedure can then be applied to different scenarios to analyze the sensitivity of a decision or to test the accuracy of an approximation method. Third, test the accuracy of seven approximation methods under a variety of circumstances. Specifically, we addressed the following questions within the context of multivariate discrete distributions: Are there new approximations that should be considered? Which approximation is the most accurate, according to different measures? How accurate are the approximations as the number of random variables increases? How accurate are they as we change the underlying dependence structure? How does accuracy improve as we add lower-order assessments? What are the implications of these findings for decision analysis practice and research? While the above questions are easy to pose, they are challenging to answer. For Decision Analysis, the answers open a new avenue to address partial information, which bing us to the last contribution. Fourth, propose a new approach to decision making with partial information. The exploration of old and new approximations and the capability of creating large collections of joint distributions that match expert assessments provide new tools that extend the field of decision analysis. In particular, we presented two sample cases that illustrate the scope of this work and its impact on uncertain decision making. / text

With the emerging nationwide availability of plug-in electric vehicles (PEVs) at prices attainable for many consumers, electric utilities, system operators, and researchers have been investigating the impact of this new source of electricity demand. The presence of PEVs on the electric grid might offer benefits equivalent to dedicated utility-scale energy storage systems by leveraging vehicles' grid-connected energy storage through vehicle-to-grid (V2G) enabled infrastructure. Existing research, however, has not effectively examined the interactions between PEVs and the electric grid in a V2G system. To address these shortcomings in the literature, longitudinal vehicle travel data are first used to identify patterns in vehicle use. This analysis showed that vehicle use patterns are distinctly different between weekends and weekdays, seasonal interactions between vehicle charging, electric load, and wind generation might be important, and that vehicle charging might increase already high peak summer electric load in Texas. Subsequent simulations of PEV charging were performed, which revealed that unscheduled charging would increase summer peak load in Texas by approximately 1\%, and that uncertainty that arises from unscheduled charging would require only limited increases in frequency regulation procurements. To assess the market potential for the implementation of a V2G system that provides frequency regulation ancillary services, and might be able to provide financial incentives to participating PEV owners, a two-stage stochastic programming formulation of a V2G system operator was created. In addition to assessing the market potential for a V2G system, the model was also designed to determine the effect of the market power of the V2G system operator on prices for frequency regulation, the effect of uncertainty in real-time vehicle availability and state-of-charge on the aggregator's ability to provide regulation services, and the effect of different vehicle characteristics on revenues. Results from this model showed that the V2G system operator could generate revenue from participation in the frequency regulation market in Texas, even when subject to the uncertainty in real-time vehicle use. The model also showed that the V2G system operator would have a significant impact on prices, and thus as the number of PEVs participating in a V2G program in a given region increased, per-vehicle revenues, and thus compensation provided to vehicle owners, would decline dramatically. From these estimated payments to PEV owners, the decision to participate in a V2G program was analyzed. The balance between the estimated payments to PEV owners for participating in a V2G program and the increased probability of being left with a depleted battery as a result of V2G operations indicate that an owner of a range-limited battery electric vehicle (BEV) would probably not be a viable candidate for joining a V2G program, while a plug-in hybrid electric vehicle (PHEV) owner might find a V2G program worthwhile. Even for a PHEV owner, however, compensation for participating in a V2G program will provide limited incentive to join. / text

An ongoing objective in healthcare is the development of tools to improve patient decision-making and surgical outcomes for patients with breast cancer that have undergone or plan to undergo breast reconstruction. In keeping with the bioethical concept of autonomy, these decision models are patient-oriented and expansive, covering a range of different patient decision-makers. In pursuit of these goals, this dissertation contributes to the development of a prototype shared decision support system that will guide patients with breast cancer and their physicians in making decisions about breast reconstruction. This dissertation applies principles in decision analysis to breast reconstruction decision-making. In this dissertation, we examine three important areas of decision-making: (1) the options available to decision-makers, (2) the validity of probabilistic information assessed from reconstructive surgeons, and (3) the feasibility of applying multiattribute utility theory. In addition, it discusses the influences of breast aesthetics and proposes a measure for quantifying such influences. The dissertation concludes with a fictional case study that demonstrates the integration of the findings and application of decision analysis in patient-oriented shared breast reconstruction decision-making. Through the implementation of decision analysis principles, cognitive biases and emotion may be attenuated, clearing the decision-maker’s judgment, and ostensibly leading to good decisions. While good decisions cannot guarantee good outcomes at the individual level, they can be expected to improve outcomes for patients with breast cancer as a whole. And regardless of the outcome, good decisions yield clarity of action and grant the decision-maker a measure of peace in an otherwise uncertain world. / text

This dissertation studies strategies to mitigate the risks associated with operational and strategic decisions of a firm, particularly focusing on project management, product development and procurement decisions. In the first essay we develop two simulation-based methods to evaluate risky capital investment projects that involve managerial flexibility. Many risky projects are characterized by significant demand and operational risks (such as learning curve uncertainty) that are difficult to capture by simple stochastic processes. We propose using system dynamics simulations to estimate the cash flow resulting from these projects and build upon prior work on real options valuation in the decision analysis literature to develop two valuation algorithms. In the second essay we explore the technology investment decisions for platforms in markets that exhibit cross-network effects. We focus on the trade-off firms must make between investing new product development resources to increase a platform's core performance and functionality versus investments designed to leverage the platform's cross-network effects. Abstracting from examples drawn from multiple industries, we use a strategic model to gain intuition about how to make such trade-off decisions under competition. In the third essay, we analyze the optimal procurement strategy of a firm that faces supply and demand risk. In particular, the firm can source from two unreliable suppliers with different delivery characteristics. We study the optimal order allocation policy shaped by the trade-offs between delivery leadtime, reliability and procurement cost. Further, we discuss the value of leadtime flexibility in supply risk mitigation and highlight the role of an inferior supplier in a firm's multi-sourcing strategy. The main contribution of this dissertation to the operations management literature is two-fold. First, it illustrates the role of effective risk mitigation through operational strategies of leadtime flexibility and supply diversification as well as through recognizing managerial flexibility. Second, it highlights the importance of leveraging third-party content development while making technology investment decisions for platforms in two-sided markets. / text

Exploration and production (E&P) involves the upstream activities from looking for promising reservoirs to extracting oil and selling it to downstream companies. E&P is the most profitable business in the oil industry. However, it is also the most capital-intensive and risky. Hence, the proper assessment of E&P projects with effective management of uncertainties is crucial to the success of any upstream business. This dissertation is concentrated on developing portfolio optimization models to manage E&P projects. The idea is not new, but it has been mostly restricted to the conceptual level due to the inherent complications to capture interactions among projects. We disentangle the complications by modeling the project portfolio optimization problem as multistage stochastic programs with mixed integer programming (MIP) techniques. Due to the disparate nature of uncertainties, we separately consider explored and unexplored oil fields. We model portfolios of real options and portfolios of decision trees for the two cases, respectively. The resulting project portfolio models provide rigorous and consistent treatments to optimally balance the total rewards and the overall risk. For explored oil fields, oil price fluctuations dominate the geologic risk. The field development process hence can be modeled and assessed as sequentially compounded options with our optimization based option pricing models. We can further model the portfolio of real options to solve the dynamic capital budgeting problem for oil projects. For unexplored oil fields, the geologic risk plays the dominating role to determine how a field is optimally explored and developed. We can model the E&P process as a decision tree in the form of an optimization model with MIP techniques. By applying the inventory-style budget constraints, we can pool multiple project-specific decision trees to get the multistage E&P project portfolio optimization (MEPPO) model. The resulting large scale MILP is efficiently solved by a decomposition-based primal heuristic algorithm. The MEPPO model requires a scenario tree to approximate the stochastic process of the geologic parameters. We apply statistical learning, Monte Carlo simulation, and scenario reduction methods to generate the scenario tree, in which prior beliefs can be progressively refined with new information. / text

Darbo aktualumas. Pastato reikšmė žmogaus gyvenime labai svarbi, nes jis negali išgyventi neturėdamas stogo virš galvos. Didžiąją laiko dalį žmogus praleidžia uždaroje patalpoje – namuose bei darbo vietoje. Nuo pastato kokybės priklauso žmogaus sveikata, savijauta bei darbingumas. Darbo tikslas – nustatyti subalansuotos statybos pastato ekologinę vertę taikant verbalinės analizės principus. Darbo uždaviniai: 1. Atlikti subalansuotos statybos ekologiniu aspektu teorinę analizę. 2. Išanalizuoti subalansuotos statybos ekologiniu aspektu vertinimo metodiką. 3. Pritaikyti verbalinės sprendimų analizės pagrindinius principus vertinant subalansuotą statybą ekologiniu aspektu. 4. Sukurti subalansuotos statybos ekologiniu aspektu vertinimo kriterijų klasifikaciją ir pasinaudoti ja. Nustatyti turimų subalansuotos statybos ekologiniu aspektu lygius pagal verbalinės sprendimo analizės metodą CLARA (realių alternatyvų klasifikacija). 5. Nustatyti pastato ekologinę vertę pagal verbalinės sprendimų analizės metodą CLARA. Tyrimo metodas. Šiame darbe taikyti du metodai. Rašant teorinę dalį, buvo susisteminta mokslinė literatūra, atlikta įvairių autorių mokslo darbų, kuriuose nagrinėjama subalansuota statyba bei jos vertinimas, ir kitos susijusios literatūros, analizė. Atliekant pastato ekologinį vertinimą buvo panaudotas verbalinės sprendimo analizės metodas CLARA. Išvados. Išanalizavus mokslinę literatūrą buvo sudaryti verbalinės sprendimo analizės kriterijai, įvesti į programą CLARĄ ir... [toliau žr. visą tekstą] / Relevance of the paper. Building is very important for man life, because he could not live without roof over head. The biggest part of the life man spends in close area – in the house and office. Man health, feeling and efficiency belong on quality of the building. The objective of the paper – to evaluate building of sustainable construction with emphasis of ecology. The goals of the paper: 1. to carry out the theoretical problem analysis of the sustainable construction; 2. to analyze the methodology of sustainable construction evaluation; 3. to use verbal decision analysis main features to evaluate sustainable construction with emphasis on ecology; 4. to create features to evaluate sustainable construction with emphasis on ecology. To use verbal decision analysis methodology CLARA to evaluate sustainable construction with emphasis on ecology. 5. to evaluate building ecology value on verbal decision analysis methodology CLARA. Conclusion. Created verbal decision analysis methodology CLARA can be used in practice. Structure: introduction, 4 sections, conclusions and suggestions, references. Thesis consist of: 67 p. text without appendixes, 15 pictures, 4 tables, 70 bibliographical entries.