The objective of this study was to develop a decision making tool to determine economic feasibility of artisan cheese operations. A survey of current Oregon artisan cheese companies was used to gain knowledge of the fixed and variable costs associated with cheese production and business start-up. The data from this survey was used to design a business model within Microsoft Excel 2010 that effectively describes the business environment in which an artisan cheese company could exist. Economic feasibility was determined through net present value (NPV) and Internal Rate of Return (IRR) of the investment. The model estimates size of the production and aging facilities based on production volume and cheese types produced. The application of the tool is demonstrated in this study through testing of several scenarios within each area of investigation: impact of milk pricing, cheese styles, product retail price, and geographical location of the creamery. The model also predicts the minimum product retail pricing necessary to ensure a positive NPV of the potential venture at several sizes of production and across several different styles of cheeses. / Graduation date: 2012

This thesis deals with application of optimisation methods based on linear and mixed-integer linear programming to various problems in the power sector related to electricity production. The thesis goal is to test the applicability of such methods to formulating and solving various instances from the class of real-world electricity production problems, and to find the advantages and disadvantages associated with using these methods. Introductory chapters describe the main characteristics of power markets, including the historical and regulatory context. Fundamental properties of power markets on both demand and supply side are also described, both from a real-world and a modelling point of view. Benefits of optimisation and modelling are discussed, in particular the solution feasibility and optimality as well as insights gained from sensitivity analysis which is often difficult to replicate with the original system. In the core of the thesis, optimisation techniques are applied to three case studies, each of which deals with a specific problem arising during electricity production. In the first problem, the profit of gas-fired power plant in Slovakia from selling power on the day-ahead market is maximised. The model is set up using both technical and commercial constraints. The second problem deals with the problem of representing a two-dimensional production function which primarily arises for a hydro generator with large variations in the level of its reservoir. Several representations of the original function using piecewise linear subsets are presented, compared, and characterised by their computational intensity both theoretically and practically. In the third problem, the prices on the German day-ahead market in 2011 are modelled. Contrary to the previous two models, the model does not capture an optimisation problem faced by a single producer, but incorporates a large subset of the whole market instead. Consequently the model is formed out of generic constraints relevant to all power plants whose parameters are estimated. By combining information about the aggregate availability of power plants with the estimated efficiencies a full supply curve for each day is created. Different scenarios are analysed to test the impact of uncertain inputs such as unknown or estimated constraints. The choice of the investigated problems stems from the attempt to cover electricity production problems from the point of view of multiple criteria. The three investigated electricity production problems span a broad range from the decisions of a single power plant to the modelling a power market as a whole. Formulations of the production function with different level of detail are presented ranging from a simple linear relationship to several bivariate function formulations. While each problem answers a specific question, they all illustrate the ease with which various electricity production problems can solved using optimisation methods based on linear and mixed-integer linear programming. This is mainly due to the ability of these methods to approximate even non-linear functions and constraints over non-convex domains and find global solutions in reasonable time. Moreover, models formulated with these methods allow sensitivity and scenario analyses to be carried out easily as is illustrated in each of the case studies.

This master´s thesis „The role and benefits of Incentives in attracting FDI in CR“ is focused on the sphere of investment incentives used as an instrument of Czech goverment policy by decision making of foreign investors to enter and start busines in the Czech Republic. This thesis gives a picture on the specific factors, which play the greatest influence on decision if foreign companies will finally invest in CR. There are four studies by evaluating company´s business plan to show influence of investment grants on start-up costs of investment comparison to obtained incentives funds and the impact on the employment in certain regions force on goverment employment policy.

This thesis examines the optimal scheduling of a hydroelectric power plant with cascaded reservoirs each with multiple generating units under uncertainty after testing three linearization methods. These linearization methods are Successive Linear Programming, Piecewise Linear Approximations, and a Hybrid of the two together. There are two goals of this work. The first goal of this work aims to replace the nonconvex mixed-integer nonlinear program (MINLP) with a computationally efficient linearized mixed-integer linear program (MILP) that will be capable of finding a high quality solution, preferably the global optimum. The second goal is to implement a stochastic approach on the linearized method in a pseudo-rolling horizon method which keeps the ending time step fixed. Overall, the Hybrid method proved to be a viable replacement and performs well in the pseudo-rolling horizon tests. / Thesis / Master of Applied Science (MASc)