We attempt to determine the structure of the automorphism group of a generic circulant graph. We first show that almost all circulant graphs have automorphism groups as small as possible. Dobson has conjectured that almost all of the remaining circulant (di)graphs (those whose automorphism groups are not as small as possible) are normal circulant (di)graphs. We show this conjecture is not true in general, but is true if we consider only those circulant (di)graphs whose orders are in a “large” subset of integers. We note that all non-normal circulant (di)graphs can be classified into two natural classes (generalized wreath products, and deleted wreath type), and show that neither of these classes contains almost every non-normal circulant digraph.

Portfolios of contemporary integrated petroleum companies consist of a few dozen Exploration and Production (E&P) projects that are usually spread all over the world. Therefore, it is important not only to manage individual projects by themselves, but to also take into account different interactions between projects in order to manage whole portfolios. This study is the step-by-step representation of the method of optimizing portfolios of risky petroleum E&P projects, an illustrated method based on Markowitz’s Portfolio Theory. This method uses the covariance matrix between projects’ expected return in order to optimize their portfolio. The developed computer model consists of four major modules. The first module generates petroleum price forecasts. In our implementation we used the price forecasting method based on Sequential Gaussian Simulation. The second module, Monte Carlo, simulates distribution of reserves and a set of expected production profiles. The third module calculates expected after tax net cash flows and estimates performance indicators for each realization, thus yielding distribution of return for each project. The fourth module estimates covariance between return distributions of individual projects and compiles them into portfolios. Using results of the fourth module, analysts can make their portfolio selection decisions. Thus, an advanced computer model for optimization of the portfolio of petroleum assets has been developed. The model is implemented in a MATLAB® computational environment and allows optimization of the portfolio using three different return measures (NPV, GRR, PI). The model has been successfully applied to the set of synthesized projects yielding reasonable solutions in all three return planes. Analysis of obtained solutions has shown that the given computer model is robust and flexible in terms of input data and output results. Its modular architecture allows further inclusion of complementary “blocks” that may solve optimization problems utilizing different measures (than considered) of risk and return as well as different input data formats.

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.