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Optimization, conservation and valuation of contingent claims in economic resource management under uncertaintyJia, Siwei 02 August 2004 (has links)
Graduation date: 2005
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Integrating real-time weather data with dynamic crop development modelsDonaldson, William S. 14 November 1991 (has links)
Crop development models are commonly used in research.
However, their use as crop management tools for growers is
rare. Decision support systems (DSS), which combine crop
models with expert systems, are being developed to provide
management assistance to growers. Researchers at Oregon
State University are in the process of developing a DSS.
Research was conducted to develop a computer program to
provide current and generated weather data for use by the
DSS. The objectives of this research were to obtain a
weather station, develop a set of quality control procedures
to check data from the station, obtain a weather generator
program, and create a weather data manager program to
implement the above objectives.
A weather station was obtained and was placed near two
existing weather stations for ten months. Data from the
weather station was compared with the other two stations for
values of monthly average maximum temperature, minimum
temperature, and daily total solar radiation and monthly
total precipitation. The weather station performed well.
Only measurements of total daily solar radiation were
consistently different from the other stations. Based on a
comparison of the weather station with an Eppley
pyranometer, a factor was calculated to correct the solar
radiation readings.
The quality control procedures used on the weather data
were adapted from automated procedures given in the
literature. When tested, the procedures performed as
desired. When used on actual data from the weather station,
values that failed the procedures were apparently legitimate
values. Options were added to the data manager program that
allow the user to quickly decide what to do with failed
values.
For a weather data generator, WGEN was chosen from the
generators presented in the literature. An input parameter
file was created for the Corvallis, Oregon area and thirty
years of data were generated. Monthly means from this data
were compared with thirty-year historical monthly means for
Corvallis. Precipitation data from WGEN compared well with
the historical data. The generated data for maximum and
minimum temperature and daily total solar radiation had
great differences from the historical data. It is believed
that the input parameters for the Corvallis area suggested
by the authors of WGEN are not appropriate.
The weather data manager program was written in the C
programming language, and occupies approximately 98
kilobytes of disk space, not including the eleven files
created directly and indirectly by the program. The main
functions of the program are: 1) retrieving data from the
weather station and performing quality control procedures on
the data (allowing the user to decide what to do with values
that failed QC); 2) viewing and editing of files by the
user; 3) weather data generation (creating a file of only
generated data or appending generated data to the file of
current data from the weather station to create a file
containing a full year of weather data); and 4)
miscellaneous functions (monitoring the weather station,
setting the calendar in the station's datalogger, and
changing information used by the data manager program).
It is hoped that this program will be a significant
contribution towards the development of a decision support
system. / Graduation date: 1992
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Transportation resource management in large-scale freight consolidation networksCarbajal Orozco, Jose Antonio 24 August 2011 (has links)
This dissertation proposes approaches that enable effective planning and control of mobile transportation resources in large-scale consolidation networks. We develop models, algorithms, and methodologies that are applied to fleet sizing and fleet repositioning. Three specific but interrelated problems are studied. The first two relate to the trade-offs between fleet size and repositioning costs in transportation resource management, while the third involves a dynamic empty repositioning problem with explicit consideration of the uncertainty of future requirements that will be revealed over time.
Chapter 1 provides an overview of freight trucking, including the consolidation trucking systems that will be the focus of this research.
Chapter 2 proposes an optimization modeling approach for analyzing the trade-off between the cost of a larger fleet of tractors and the cost of repositioning tractors for a trucking company operating a consolidation network, such as a less-than-truckload (LTL) company. Specifically, we analyze the value of using extra tractor repositioning moves (in addition to the ones required to balance resources throughout the network) to attain savings in the fixed costs of owning or leasing a tractor fleet during a planning horizon. The primary contributions of the research in this chapter are that (1) we develop the first optimization models that explore the impact of fleet size reductions via repositioning strategies that have regularity and repeatability properties, and (2) we demonstrate that substantial savings in operational costs can be achieved by repositioning tractors in anticipation of regional changes in freight demand.
Chapter 3 studies the optimal Pareto frontiers between the fleet size and repositioning costs of resources required to perform a fixed aperiodic or periodic schedule of transportation requests. We model resource schedules in two alternative ways: as flows on event-based, time-expanded networks; and as perfect matchings on bipartite networks. The main contributions from this chapter are that (1) we develop an efficient re-optimization procedure to compute adjacent Pareto points that significantly reduces the time to compute the entire Pareto frontier of fleet size versus repositioning costs in aperiodic networks, (2) we show that the natural extension to compute adjacent Pareto points in periodic networks does not work in general as it may increase the fleet size by more than one unit, and (3) we demonstrate that the perfect matching modeling framework is frequently intractable for large-scale instances.
Chapter 4 considers robust models for dynamic empty-trailer repositioning problems in very large-scale consolidation networks. We investigate approaches that deploy two-stage robust optimization models in a rolling horizon framework to address a multistage dynamic empty repositioning problem in which information is revealed over time. Using real data from a national package/parcel express carrier, we develop and use a simulation to evaluate the performance of repositioning plans in terms of unmet loaded requests and execution costs. The main contributions from this chapter are that (1) we develop approaches for embedding two-stage robust optimization models within a rolling horizon framework for dynamic empty repositioning, (2) we demonstrate that such approaches enable the solution of very large-scale instances, and (3) we show that less conservative implementations of robust optimization models are required within rolling horizon frameworks.
Finally, Chapter 5 summarizes the main conclusions from this dissertation and discusses directions for further research.
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Game-theoretic coordination and configuration of multi-level supply chainsHuang, Yun, 黄赟 January 2010 (has links)
published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy
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Asset pricing, hedging and portfolio optimizationFu, Jun, 付君 January 2012 (has links)
Starting from the most famous Black-Scholes model for the underlying asset
price, there has been a large variety of extensions made in recent decades.
One main strand is about the models which allow a jump component in the
asset price. The first topic of this thesis is about the study of jump risk
premium by an equilibrium approach. Different from others, this work provides
a more general result by modeling the underlying asset price as the ordinary
exponential of a L?vy process. For any given asset price process, the equity
premium, pricing kernel and an equilibrium option pricing formula can be
derived. Moreover, some empirical evidence such as the negative variance risk
premium, implied volatility smirk, and negative skewness risk premium can
be well explained by using the relation between the physical and risk-neutral
distributions for the jump component.
Another strand of the extensions of the Black-Scholes model is about the
models which can incorporate stochastic volatility in the asset price. The second
topic of this thesis is about the replication of exponential variance, where
the key risks are the ones induced by the stochastic volatility and moreover it
can be correlated with the returns of the asset, referred to as leverage effect.
A time-changed L?vy process is used to incorporate jumps, stochastic volatility
and leverage effect all together. The exponential variance can be robustly
replicated by European portfolios, without any specification of a model for the
stochastic volatility.
Beyond the above asset pricing and hedging, portfolio optimization is also
discussed. Based on the Merton (1969, 1971)'s reduced portfolio optimization
and the delta hedging problem, a portfolio of an option, the underlying stock
and a risk-free bond can be optimized in discrete time and its optimal solution
can be shown to be a mixture of the Merton's result and the delta hedging
strategy. The main approach is the elasticity approach, which has initially
been proposed in continuous time.
In addition to the above optimization problem in discrete time, the same
topic but in a continuous-time regime-switching market is also presented. The
use of regime-switching makes our market incomplete, and makes it difficult to
use some approaches which are applicable in complete market. To overcome
this challenge, two methods are provided. The first method is that we simply
do not price the regime-switching risk when obtaining the risk-neutral probability.
Then by the idea of elasticity, the utility maximization problem can be
formulated as a stochastic control problem with only a single control variable,
and explicit solutions can be obtained. The second method is to introduce
a functional operator to general value functions of stochastic control problem
in such a way that the optimal value function in our setting can be given by
the limit of a sequence of value functions defined by iterating the operator.
Hence the original problem can be deduced to an auxiliary optimization problem,
which can be solved as if we were in a single-regime market, which is
complete. / published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy
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Identification of vulnerable transportation infrastructure and household decision making under emergency evacuation conditionsMurray-Tuite, Pamela Marie 28 August 2008 (has links)
Not available / text
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Evolutionary optimisation of industrial systems梁慧敏, Leung, Wai-man, Wanthy. January 1999 (has links)
published_or_final_version / abstract / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy
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Assessment of forest stocking conditions by multiple-stage remote sensing techniquesBisson, Henri Robert, 1947- January 1975 (has links)
No description available.
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Toward verification of a natural resource uncertainty modelDavis, Trevor John 11 1900 (has links)
Natural resource management models simplify reality for the purpose of planning or management.
In much the same way, an uncertainty model simplifies the many uncertainties that
pervade the natural resource management model. However, though a number of uncertainty
models have been developed, there has been little work on verifying such models against the
uncertainty they purport to represent. The central research question addressed by this work is
'can a natural resource management uncertainty model be verified in order to evaluate its
utility in real-world management?' Methods to verity uncertainty models are developed in two
areas: uncertainty data models, and uncertainty propagation through process models. General
methods are developed, and then applied to a specific case study: slope stability uncertainty in
the southern Queen Charlotte Islands. Verification of two typical uncertainty data models (of
classified soils and continuous slope) demonstrates that (in this case) both expert opinion
inputs and published error statistics underestimate the level of uncertainty that exists in
reality. Methods are developed to recalibrate the data models, and the recalibrated data are
used as input to an uncertainty propagation model. Exploratory analysis methods are then
used to verify the output of this model, comparing it with a high-resolution mass wastage
database—itself developed using a new set of tools incorporating uncertainty visualisation.
Exploratory data analysis and statistical analysis of the verification shows that, given the
nature of slope stability modelling, it is not possible to directly verify variability in the model
outputs due to the existing distribution of slope variability (based on the nature of slope modelling).
However, the verification work indicates that the information retained in uncertaintybased
process models allows increased predictive accuracy—in this case of slope failure. It is
noted that these verified models and their data increase real-world management and planning
options at all levels of resource management. Operational utility is demonstrated throughout
this work. Increased strategic planning utility is discussed, and a call is made for integrative
studies of uncertainty model verification at this level.
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Partial ordering of risky choices : anchoring, preference for flexibility and applications to asset pricingSagi, Jacob S. 11 1900 (has links)
This dissertation describes two theories of risky choice based on a normatively axiomatized
partial order. The first theory is an atemporal alternative to von Neumann
and Morgenstern's Expected Utility Theory that accommodates the status quo bias, violations
of Independence and preference reversals. The second theory is an extension of
the Inter-temporal von Neumann-Morgenstern theory of Kreps and Porteus (1978) that
features a normatively deduced preference for flexibility. A substantial part of the thesis
is devoted to examining equilibrium implications of the inter-temporal theory. In particular,
a multi-agent multi-period Bayesian rational expectations equilibrium is shown to
exist under certain conditions. Implications to asset pricing are then investigated with
an explicit parameterization of the model.
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