The in-flight catering industry is a major contributor to food wastage. This wastage is a direct result of the deliberate overproduction of in-flight meals to protect against meal shortages and dissatisfied passengers. With the global strive towards sustainability and the resulting impact of wastage on a company's corporate image, in-flight catering companies need a solution that strives to achieve zero waste and a 100% passenger satisfaction level. This dissertation evaluates the value of combining product substitution and demand uncertainty within an inventory decision-making model as a potential solution opportunity for the wastage dilemma faced by the in-flight catering industry. The decision-making model's purpose is to assist in-flight caterers to make improved decisions regarding the quantity of each meal type to produce for the specific flight under consideration. The model developed is defined as a stochastic multi-objective mixed-integer programming model with fixed recourse and two-way, stock-out based, partial consumer-driven (static) product substitution. The model relies on the output of a forecasting model, that consists of a time-inhomogeneous Markov Chain and a multiple regression model, to forecast the probability distribution of a flight's aggregate meal demand. Due to the lack of available data from public sources, synthetic data is generated to evaluate the model developed. The model is compared against three alternative models that lack either demand uncertainty, product substitution or both to validate the value of including these elements in the decision-making model. The comparison results indicate that the inclusion of the passenger load uncertainty improves the model's average reliability to achieve a 92% minimum passenger satisfaction level with at least 9.2%. Furthermore, it is shown that the stochastic passenger load model produces an average of 2.2 fewer surplus meals per flight instance at the expense of a 3.3% lower reliability when including the substitution behaviour of passengers. This substitution model's superior waste minimisation is attributed to the model's inherent risk-pooling capabilities, and further analysis shows that the value of product substitution increases when the model becomes more constrained. It is, therefore, concluded that the value of product substitution depends on the in-flight caterer's bias towards maximising either reliability or performance. / Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2021. / Council for Scientific and Industrial Research (CSIR) / Industrial and Systems Engineering / MEng / Unrestricted

This thesis presents two Models of portfolio optimization, namely the Markowitz Mean Variance Optimization Model and the Rockefeller and Uryasev CVaR Optimization Model. It then presents an application of these models to a portfolio of clean energy assets for optimal allocation of financial resources in terms of maximum returns and low risk. This is done by writing GAMS programs for these optimization problems. An in-depth analysis of the results is conducted, and we see that the difference between both models is not very significant even though these results are data-specific.

The thesis deals with possibilities of mathematical modeling for public university budgets. Firstly, external conditions of public university financial inflows are discussed and~illustrated by using particular data. The related basic legislature is introduced. In~the~next part, internal financial flows are described the help of a general scheme. Step-by-step, a mathematical model of the university budget was built with using analysis of existing data, rules and formulas. The proposed model represents nonlinear multi–stage scenario-based stochastic programme, involving linear and network-flow like constraints. It allows to take into account more objective functions and related parametric analysis. The model was implemented in the algebraic modeling system GAMS with~interface to MS Excel. The aim of the presented mathematical model was not to offer a tool that would be used for automatic distribution of financial resources of the university, but to give flexible possibilities to its user to realize computational experiments and in this way to achieve a deeper insight into the modeled problem.

This thesis deals with the evaluation of the strategic investment in the waste-to-energy plant development. The central supply of heat and the incineration plant connection can be provided for example by the distribution network. The objective is to find financially feasible solution regarding uncertain development of waste management and energy market. A heat supplies to district heating network significantly influences the strategic decision. A two-stage stochastic programming based on the scenarios and the GAMS software were applied to solve this task. The main contribution of this thesis is decision on crucial parameters of the waste-to-energy plant.

Tato diplomová práce uvádí základní poznatky matematického a především stochastického programování. Navíc se zabývá použitím softwaru AIMMS při vytváření a řešení optimalizačních problémů. Naším hlavním cílem je naprogramovat v softwaru AIMMS několik metod řešení problémů stochastického programování a ukázat jejich použití a užitečnost na vybraných problémech. Jedním z problémů, který jsme si zvolili, je model spalovny. Všechny AIMMS programy, které v našem textu použijeme a popíšeme, a jejich zdrojové kódy budou přiloženy v dodatcích.

This thesis deals with methods of stochastic programming and their application in financial investment. Theoretical part is devoted to basic terms of mathematical optimization, stochastic programming and decision making under uncertainty. Furter, there are introduced basic principles of modern portfolio theory, substantial part is devoted to risk measurement techniques in the context of investment, mostly to the methods Value at Risk and Expected shortfall. Practical part aims to creation of optimization models with an emphasis to minimize investment risk. Created models deal with real data and they are solved in optimization software GAMS.

PhD thesis deals with the application of the simulation and optimization methods in the waste-to-energy field. An introduction describes the current state of the waste management in the EU with the focus on the Czech Republic. In the following chapter the evaluation criteria for investment intentions and the basic principles of stochastic programming are discussed. The core of the work lays in the mathematical models for the planning and operation of the process plants as well as in the mathematical models for the waste collection. The transportation problem involves all considered technological elements and therefore it is possible to simulate the waste streams between the producers and processors. This approach is demonstrated with five case studies. In the first three studies the calculations for the potential investor are presented. The main outcome of these case studies is the determination of the level of attractiveness of investment and the identification the greatest risks. Another case study is devoted to an analysis with the focus on perspective of government policies and in the last case study the issue of the waste management is analyzed in detail from the perspective of the waste producers. Developed computational tools are flexible and can be further developed and adapted based on the objectives of the specific tasks.

PhD (Financial Management) / Department of Mathematics and Applied Mathematics / See the attached abstract below

Stochastic methods are present in our daily lives, especially when we need to make a decision based on uncertain events. In this thesis, we present basic approaches used in stochastic tasks. In the first chapter, we define the stochastic problem and introduce basic methods and tasks which are present in the literature. In the second chapter, we present various problems which are non-linearly dependent on the probability measure. Moreover, we introduce deterministic and non-deterministic multicriteria tasks. In the third chapter, we give an insight on the concept of stochastic dominance and we describe the methods that are used in tasks with multidimensional stochastic dominance. In the fourth chapter, we capitalize on the knowledge from chapters two and three and we try to solve the role of portfolio optimization on real data using different approaches. 1

The thesis deals with the algorithms for two-stage stochastic programs. The first chapter considers the basic properties and theory. Specifically, we introduce the properites of the feasibility region and the objective function. Further, optimality conditions are discussed. In the second chapter we present algoritms which can be used to solve two-stage linear programs with fixed recourse. In the first section the basic L-shaped method is described in detail. The second section provides an explanation of the Stochastic Decomposition algorithm with the inclusion of a regularization term. The last chapter presents computational results. Three practical examples are provided both with a brief description of the problem and solutions by the studied algorithms.