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The use of soft computing as support for business decision-making
Pekárek, Jan ; Jašek, Roman (referee) ; Lenort, Radim (referee) ; Zinecker, Marek (referee) ; Dostál, Petr (advisor)
The presented dissertation deals with the problem of deploying the charging infrastructure for electric vehicles in the Czech Republic. The core of the thesis is a mathematical optimization model, which is implemented in the language of MATLAB computing software. The model consists of several sub-units representing separate models of studied sub-problems. The individual chapters of the work describe successively these sub models. The sub models are: demand model of the charging service, model of charging supply, charging simulator model, optimization model and its resolving optimization method. The optimization model is accelerated by parallelization on the graphics card. The optimization method is designed as a case-specific implementation of genetic algorithms on a population of tree-structured individuals. The final chapter deals with an economic aspect of the problem under consideration, the implications of the findings and the role that the optimization model plays in the context under consideration. The main benefit of the work lies in the formulation of the problem as a mathematical model, the accompanying analyses and the provided justifications. Any user with updated data can then use this work along with the attached scripts to find answers to questions about the relationship between electromobility and the charging infrastructure.
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The use of soft computing as support for business decision-making
Pekárek, Jan ; Jašek, Roman (referee) ; Lenort, Radim (referee) ; Zinecker, Marek (referee) ; Dostál, Petr (advisor)
The presented dissertation deals with the problem of deploying the charging infrastructure for electric vehicles in the Czech Republic. The core of the thesis is a mathematical optimization model, which is implemented in the language of MATLAB computing software. The model consists of several sub-units representing separate models of studied sub-problems. The individual chapters of the work describe successively these sub models. The sub models are: demand model of the charging service, model of charging supply, charging simulator model, optimization model and its resolving optimization method. The optimization model is accelerated by parallelization on the graphics card. The optimization method is designed as a case-specific implementation of genetic algorithms on a population of tree-structured individuals. The final chapter deals with an economic aspect of the problem under consideration, the implications of the findings and the role that the optimization model plays in the context under consideration. The main benefit of the work lies in the formulation of the problem as a mathematical model, the accompanying analyses and the provided justifications. Any user with updated data can then use this work along with the attached scripts to find answers to questions about the relationship between electromobility and the charging infrastructure.
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