Systematic studies of RF phase modulation at KARA

Die Lebensdauer des Elektronenstrahl am Karlsruhe Research Accelerator (KARA) Speichering, Karlsruhe Institute of Technology (KIT) ist hauptsächlich durch die Streuung an Restgasatomen limitiert, wobei auch die Touschek-Streuung zwischen zwei Elektronen des selben Elektronenpakets die Lebensdauer beschränkt. Dieser Effekt hängt direkt von der Elektronendichte ab. In früheren Arbeiten ist bereits herausgefunden worden, dass eine Verlängerung der Elektronenpakete durch eine Modulation der RF Beschleunigungsphase erreicht werden kann, wobei longitudinale Oszillationsmoden innerhalb der Pakete angeregt werden. Um die optimalen Bedingungen zur Einführung der Phasenmodulation (PM) in den Beschleuniger-Betrieb zu finden, ist in dieser Arbeit ist eine erste systematische Untersuchung der (PM) in der Nähe der zweiten Harmonischen der Synchrotronoszillationsfrequenz mit einer Amplitude von 10% bis 20% der synchronen Phase durchgeführt worden. Dabei ist ein besonderer Fokus auf dem Einfluss der Phasenmodulation auf die Paketform, die Paketlänge und folglich die Lebensdauer gelegt worden

Valuing infrastructure investments as portfolios of interdependent real options

The value of infrastructure investments is frequently influenced by enormous uncertainty surrounding both exogenous and endogenous factors. At the same time, however, their value is generally driven by much flexibility - i.e. options - with respect to design, financing, construction and operation. Real options analysis aims to pro-actively manage risks by valuing the flexibilities inherent in uncertain investments. Although real options generally occur within portfolios whose value is affected by both exogenous and endogenous uncertainty, most existing valuation approaches focus on single (i.e. individual) options and consider only exogenous uncertainty. In this thesis, we introduce an approach for modelling and approximating the value of portfolios of interdependent real options under exogenous uncertainty, using both influence diagrams and simulation-and-regression. The key features of this approach are that it translates the interdependencies between real options into linear constraints and then integrates these in a portfolio optimisation problem, formulated as a multi-stage stochastic integer programme. To approximate the value of this optimisation problem we present a transparent valuation algorithm based on simulation and parametric regression that explicitly takes into account the state variable's multidimensional resource component. We operationalise this approach using three numerical examples of increasing complexity: an American put option in a simple single-factor setting; a natural resource investment with a switching option in a one-factor setting; and the same investment in a three-factor setting. Subsequently, we demonstrate the ability of the proposed approach to evaluate a complex natural resource investment that features both a large portfolio of interdependent real options and four underlying uncertainties. We show how our approach can be used to investigate the way in which the value of that portfolio and its individual real options are affected by the underlying operating margin and the degrees of different uncertainties. Lastly, we extend this approach to include endogenous, decision- and state-dependent uncertainties. We present an efficient valuation algorithm that is more transparent than those used in existing approaches; by exploiting the problem structure it explicitly accounts for the path dependencies of the state variables. The applicability of the extended approach to complex investment projects is illustrated by valuing an urban infrastructure investment. We show the way in which the optimal value of the portfolio and its single, well-defined options are affected by the initial operating revenues, and by the degrees of exogenous and endogenous uncertainty.Open Acces

Assortment optimization using an attraction model in an omnichannel environment

Making assortment decisions is becoming an increasingly difficult task for many retailers worldwide as they implement omnichannel initiatives. Discrete choice modeling lies at the core of this challenge, yet existing models do not sufficiently account for the complex shopping behavior of customers in an omnichannel environment. In this paper, we introduce a discrete choice model called the multichannel attraction model (MAM). A key feature of the MAM is that it specifically accounts for both the product substitution behavior of customers within each channel and the switching behavior between channels. We formulate the corresponding assortment optimization problem as a mixed integer linear program and provide a computationally efficient heuristic method that can be readily used for obtaining high-quality solutions in large-scale omnichannel environments. We also present three different methods to estimate the MAM parameters based on aggregate sales transaction data. Finally, we describe general effects of the implementation of widely-used omnichannel initiatives on the MAM parameters, and carry out numerical experiments to explore the structure of optimal assortments, thereby gaining new insights into omnichannel assortment optimization. Our work provides the analytical framework for future studies to assess the impact of different omnichannel initiatives