Saman Hassanzadeh Amin, Samantha Mulligan-Gow, and Guoqing Zhang
Chapters, 2020.
Subjects
food optimization, diet, nutrition, multi-objective programming, and uncertainty
Abstract
It is a problem that concerns us all: what should we eat on a day-to-day basis to meet our health goals? Scientists have been utilizing mathematical programming to answer this question. Through the use of operations research techniques, it is possible to find a list of foods that, in a certain quantity, can provide all nutrient recommendations in a day. In this research, a multi-objective programming model is provided to determine the selected food items for a diet problem. Two solution approaches are developed to solve this problem including weighted-sums and ?-constraint methods. Two sources of uncertainty have been considered in the model. To handle these sources, a scenario-based approach is utilized. The application of this model is shown using a case study in Canada. Using the proposed model and the solution approaches, the best food items can be selected and purchased to minimize the total cost and maximize health.
artificial intelligence system, method, probability, risk, and uncertainty
Abstract
As a result of the analysis of dispatcher intelligence centers and aerial, land, underground, underwater, universal, and functionally focused artificial intelligence robotics systems, the problems of rational control, due to be performed under specific conditions of uncertainties, are chosen for probabilistic study. The choice covers the problems of planning the possibilities of functions performance on the base of monitored information about events and conditions and the problem of robot route optimization under limitations on risk of "failure" in conditions of uncertainties. These problems are resolved with a use of the proposed probabilistic approach. The proposed methods are based on selected probabilistic models (for "black box" and complex systems), which are implemented effectively in wide application areas. The cognitive solving of problems consists in improvements, accumulation, analysis, and use of appearing knowledge. The described analytical solutions are demonstrated by practical examples.
Forecasting, Nowcasting, Markov-Switching Dynamic Factor Model, BVAR, Threshold VAR, Time-Varying Parameter, Mixed-frequency Models, Bayesian Methods, Turning Points, Great Recession, Great Moderation, Counterfactuals, Stochastic Volatility, and Uncertainty
Abstract
Im Zentrum dieser Dissertation steht das Beschreiben und Erklären von Konjunkturdynamiken. Motiviert durch den außerordentlich starken wirtschaftlichen Einbruch in 2008/2009 betont die Arbeit dabei die Wichtigkeit der Nutzung von nichtlinearen Modellansätzen. Die Dissertation kann als Beitrag im Bereich der angewandten Makroökonometrie gesehen werden und strukturiert sich in zwei Kategorien. Die Artikel in Kapitel eins und zwei bestehen aus angewandten Studien und untersuchen Modellansätze für die deutsche Wirtschaft. Die Artikel in den Kapiteln drei und vier bestehen aus methodischen Arbeiten insbesondere hinsichtlich ökonomischer Prognosen.
