|
|
The travelling thief problem
Ternbach, Pavel ; Dosoudilová, Monika (referee) ; Kůdela, Jakub (advisor)
Recently, the field of algorithm optimization has been addressing a problem of large number of optimization problems not being nearly as complex as some real-world problems. These real-world problems are increasing in complexity, while the optimization problems are outdated. In order to understand and find better ways of solving these complex real-world problems, the travelling thief problem, also known by the acronym "TTP", was created. Travelling thief problem was designed to resemble real-world problems as closely as possible by combining two subproblems. Since solving the TTP is relatively difficult, various algorithms using different approaches have been developed. This thesis focuses on explaining and then comparing some of these algorithms.
|
|
|
Evolutionary Optimization of Freight Transportation
Beránek, Michal ; Drahošová, Michaela (referee) ; Bidlo, Michal (advisor)
The following thesis deals with optimization of freight transport planning. The goal is to minimize expenses connected to transportation, which emerge from travelled distance. The expenses can be heavily reduced, if the routes are correctly planned, especially when there is a large number of customers to be served. This thesis focuses on solving the problem by using the evolutional algorithms, that are optimization methods based on principles of evolution. Thesis concentrates on Heterogeneous Fixed Fleet Vehicle Routing Problem. Thesis introduces multiple evolutional algorithms and their results are compared. The best algorithm, evolutional strategy with local neighbourhood search, achieves similar, for certain tasks even better results, than other existing evolutional algorithms, created to solve given problem.
|
|
|
Evolutionary Optimization of Freight Transportation
Beránek, Michal ; Drahošová, Michaela (referee) ; Bidlo, Michal (advisor)
The following thesis deals with optimization of freight transport planning. The goal is to minimize expenses connected to transportation, which emerge from travelled distance. The expenses can be heavily reduced, if the routes are correctly planned, especially when there is a large number of customers to be served. This thesis focuses on solving the problem by using the evolutional algorithms, that are optimization methods based on principles of evolution. Thesis concentrates on Heterogeneous Fixed Fleet Vehicle Routing Problem. Thesis introduces multiple evolutional algorithms and their results are compared. The best algorithm, evolutional strategy with local neighbourhood search, achieves similar, for certain tasks even better results, than other existing evolutional algorithms, created to solve given problem.
|
|
|
Acceleration of Discrete Optimization Heuristics Using GPU
Pecháček, Václav ; Jaroš, Jiří (referee) ; Pospíchal, Petr (advisor)
Thesis deals with discrete optimization problems. It focusses on faster ways to find good solutions by means of heuristics and parallel processing. Based on ant colony optimization (ACO) algorithm coupled with k-optimization local search approach, it aims at massively parallel computing on graphics processors provided by Nvidia CUDA platform. Well-known travelling salesman problem (TSP) is used as a case study. Solution is based on dividing task into subproblems using tour-based partitioning, parallel processing of distinct parts and their consecutive recombination. Provided parallel code can perform computation more than seventeen times faster than the sequential version.
|
guest ::
