The aim of this diploma thesis is to design and efficiently implement a framework to support optimization modelling. The emphasis is placed on two-stage stochastic optimization problems and performing calculations on large data. The computing core uses the GAMS system and with using its application interface and Python programming language, the user will be able to efficiently acquire and process input and output data. The separation of the data logic and the application logic then offers a wide range of options for testing and experimenting with a general model on dynamically changing input data. The thesis is also focused on an evaluation of the framework complexity. The framework performance was evaluated by measuring the time required to complete the required task for various use cases, on the increasing sample size of input data.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:446785 |
| Date | January 2021 |
| Creators | Kovalčík, Marek |
| Contributors | Štětina, Josef, Popela, Pavel |
| Publisher | Vysoké učení technické v Brně. Ústav soudního inženýrství |
| Source Sets | Czech ETDs |
| Language | Czech |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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