This thesis investigates how estimates of reservoir compartmentalization impact facility sizing decisions and the degree to which inaccurate estimates destroy project value. An uncertainty analysis workflow is proposed and an asset development optimization model is specified to simulate the decision making process during concept selection. The model endogenizes drilling decisions and includes a real option to expand facility capacity after the uncertain variables are realized. The value of information analysis suggests that cost of erroneous estimates of reservoir compartmentalization is significant and can reduce asset value by more than 30%. We also find that the negative impacts are larger when the degree of compartmentalization is underestimated (too optimistic) than when it is overestimated (too pessimistic). / text

In this study we consider a two-echelon supply chain with a nonstationary demand process. The retailer batches the customer demand for a predetermined number of periods before placing an order to the supplier. We show that the demand process for the supplier is more variable than that for the retailer. It is observed that the supplier can reduce the variability of orders by tracking the exogenous demand occurring at the retailer&#039 / s side.

In the age of Web 2.0 users contribute to platforms success by providing personal information by actively uploading information (e.g. messages, preferences, biographies) and also by leaving traces of their online behavoiour as can be derived from their clicks, navigation paths, etc. While there is a market for trading such information among companies, there is little knowledge about how users actually value their personal information. In an online survey-based experiment we have asked 1.045 Facebook users how much they would be willing to pay for keeping their personal information. Surprisingly, 48.1 percent of participants are not willing to pay a single Euro, - thus, valuing their information at zero. Results indicate that people that show 'spamming' behaviour and users that use Facebook for 'diary keeping' are significantly more willing to pay a certain amount higher than zero to be able to keep their personal Facebook information. Interestingly, having analysed various kinds of user behaviour, the regression model still explains no more than 14.2 percent of variance. Additionally, this article discusses four different method manipulations for eliciting people's willingness to pay for personal information and provides methodical guidance for future research in the field.

In this dissertation, I investigate the value of information to consumers, the pricing of chicken, and the value of shopping lists to consumers. My first essay finds that across 14 different product categories and seven types of information, information about price and origin are the most important and information about social and environmental impacts are the least important. Our estimates also suggest consumers are willing to wait a large amount of time to obtain the most vs. least desirable types of information prior to making a non-hypothetical product choice. My second essay relates to price indices used to value chicken in the United States. There were two main price indices commonly used by the industry in recent history: the USDA 12-City Price Index and the Georgia Dock Price. We find that there was a long standing equilibrium relationship between these two price indices that shifted across time. Additionally, our analysis shows that there was a structural break between these two price indices around 2000. After this structural break, the analysis suggests Georgia Dock prices were about $0.047/lb higher than they would have been without the break. Last, my third essay aims to determine the impact of shopping lists on consumer spending and healthy shopping behaviors. We find that after controlling for conscientiousness, consumers willingness-to-accept to give up their shopping list is $5.05, while the equivalent gain to write a shopping list is $3.87. We did not find a significant difference in the healthiness of the purchases made by consumers using a shopping list versus those who shopped without a list.

Existing approaches to continuous optimization are essentially mechanisms for deciding which locations should be sampled in order to obtain information about a target function's global optimum. These methods, while often effective in particular domains, generally base their decisions on heuristics developed in consideration of ill-defined desiderata rather than on explicitly defined goals or models of the available information that may be used to achieve them. The problem of numerical optimization is essentially one of deciding what information to gather, then using that information to infer the location of the global optimum. That being the case, it makes sense to model the problem using the language of decision theory and Bayesian inference. The contribution of this work is precisely such a model of the optimization problem, a model that explicitly describes information relationships, admits clear expression of the target function class as dictated by No Free Lunch, and makes rational and mathematically principled use of utility and cost. The result is an algorithm that displays surprisingly sophisticated behavior when supplied with simple and straightforward declarations of the function class and the utilities and costs of sampling. In short, this work intimates that continuous optimization is equivalent to statistical inference and decision theory, and the result of viewing the problem in this way has concrete theoretical and practical benefits.

This research spans a variety of research topics with a common theme, providing decision support through the development and analysis of methods that assist decision making for natural resource and wildlife management. I used components of structured decision making and decision analysis to address natural resources management problems, specifically monitoring and estimating the status of harvested populations, as well as data collection decisions for landscape conservation. My results have implications for the way populations are monitored and their status is estimated. I find that the inclusion of error in data collection can have a substantial impact of the performance of abundance and growth rate estimates of harvested species and that the selection of estimation methods depends on what management objectives are most important. For example, the Sex-Age-Kill population estimation method best estimates the size of populations, while the Downing population reconstruction method better estimates trends in population growth rates. I provide a framework to support selection of the best estimation method while considering a monitoring program as a whole. Based on this framework the Vermont Fish and Wildlife Department will obtain the most benefits from a monitoring program including necropsy analysis that uses the Downing method to track population status. Finally, I demonstrated the use of value of information analysis as a tool to determine the relative expected benefits of addition spatial data collection for use in landscape mapping and conservation. This type of analysis can provide conservation agencies with a planning tool to direct budgets and mapping efforts.

The long-term success of wildlife conservation depends on maximizing the benefits of limited funds and data in pursuit of population and habitat objectives. The ultimate currency for wildlife management is progress toward long-term preservation of ample, wild, free wildlife populations and to this end, funds must be wisely spent and maximal use made from limited data. Through simulation-based analyses, I evaluated the efficacy of various models for estimating population abundance from harvest data. Because managers have different estimators to choose from and can also elect to collect additional data, I compared the statistical performance of different estimation strategies (estimator + dataset) relative to the financial cost of data collection. I also performed a value of information analysis to measure the impact that different strategies have on a representative harvest management decision. The latter analysis is not based on the cost of data, but rather on the management benefit derived from basing decisions on different datasets. Finally, I developed a hybrid modeling framework for mapping habitat quality or suitability. This framework makes efficient use of expert opinion and empirical validation data in a single, updatable statistical structure. I illustrate this method by applying it across an entire state.

The thesis is dealing with the methods of management of an enterprise and its information system. The main goal of this thesis is to depict the ties of those methods. The other goals are derived from the main one. They are structuring management methods and evaluating the appropriateness for managing IT in the small and medium enterprise. The first step for thorough mapping of the ties is structuring the methods into clearly defined approaches. The criteria gained from the systematic approach to enterprise are used for the analysis of the management methods. The mainly used management methods which have the impact on operational activity and distinct innovative character enter the analysis. In the next chapter, the basic ties of the approaches to the department of business informatics, information system and to the methods of business informatics are set. Subsequently the outputs relevant to business informatics management are extracted. The main contribution of the thesis lies in the systematic approach to methods of management which enables further research of ties to information system and its management. The second more concrete contribution lies in basic definition of the conclusions for the needs of business informatics.

Returned products of many industries are transported backwards through supply chains for recovery, thus forming &ldquo / closed-loop supply chains&rdquo / . Benefits, forthcoming with more effective management of recovery of returns are gaining importance. However, some issues, such as lack of information required to assess the quality of the returned products, may translate into critical uncertainties in the product recovery decisions and prevent closed-loop supply chains from operating efficiently. Hence, it is envisaged that significant economies may be attained by increasing the quantity of information fed into the planning decisions related to returned products. Thus, the objective of this study is to test the hypothesis that ready availability of perfect quality grade information associated with returned products by means of &ldquo / embedded systems&rdquo / , may lead to improved over all performance of recovery operations. To this end, in this thesis, linear programming models of generic multistage recovery processes are built. It is demonstrated by computational studies that significant gains may be obtained especially in environments where the prices of recovered products are decreasing in time.

Advancements in information technology enabled to track real time data in a more accurate and precise way in many manufacturing facilities. However, before obtaining the more accurate and precise data, the investment in information technology should be validated. Value of information may be adopted as a criterion in this investment. In this study, we analyze the value of information in a manufacturing facility where production and lead time quotation decisions are taken. In order to assess the value of information, two settings are analyzed. Under the first setting, the manufacturer takes decisions under perfect information. To find the optimal decisions under perfect information, a stochastic model is introduced. Under the second setting, the manufacturer takes decisions under imperfect information. To obtain a solution for this problem, Partially Observable Markov Decision Process is employed. Under the second setting, we study two approaches. In the first approach, we introduce a nonlinear programming model to find the optimal decisions. In the second approach, a heuristic approach, constructed on optimal actions taken under perfect information is presented. We examine the value of information under different parameters by considering the policies under nonlinear programming model and heuristic approach. The profit gap between the two policies is investigated. The effect of Make-to-Order (MTO) and Make-to-Stock (MTS) schemes on the value of information is analyzed. Lastly, different lead time quotation schemes / accept-all, accept-reject and precise lead time / are compared to find under which quotation scheme value of information is highest.