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Meta-Heuristic Solution in RCPSP
Šebek, Petr ; Kočí, Radek (referee) ; Hrubý, Martin (advisor)
This thesis deals with the description of the state of resource-constrained project scheduling problem. It defines the formal problem and its complexity. It also describes variants of this problem. Algorithms for solving RCPSP are presented. Heuristic genetic algorithm GARTH is analyzed in depth. The implementation of prototypes solving RCPSP using GARTH is outlined. Several improvements to the original algorithm are designed and evaluated.
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The global optimalization methods
Dudová, Aneta ; Kozumplík, Jiří (referee) ; Mézl, Martin (advisor)
This bachelor work is dedicated to advanced methods of global optimization, and especially problem traveling salesman. It focuses on the description of the problem and its various options, including graph theory, heuristic algorithms, evolutionary algorithms, in which mainly genetic algorithms and optimization by ant colonies. In conclusion, the implementation of these methods and performed testing on different data sets of algorithms that approximately solve the traveling salesman problem.
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Heuristic Algorithms in Optimization
Komínek, Jan ; Šeda, Miloš (referee) ; Roupec, Jan (advisor)
This diploma thesis deals with genetic algorithms and their properties. Particular emphasis is placed on finding the influence of mutation and population size. Genetic algorithms are applied on inverse heat conduction problems (IHCP) in the second part of the thesis. Several different approaches and coding methods were tested. Properties of genetic algorithms were improved by definition of two new genetic operators – manipulation and sorting. Reported theoretical findings were tested on the real data of inverse heat conduction problem. The library for easy implementation of GA for solving general optimization problems in C ++ was created and is described in the last chapter.
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System for Advanced Scheduling
Horký, Aleš ; Jaroš, Jiří (referee) ; Drahošová, Michaela (advisor)
This master thesis deals with the automatic design of examinations and courses scheduling. The design is adapted to the specific requirements of the Faculty of Information Technology of Brno University of Technology. A genetic algorithm and a heuristic algorithm are employed to solve this task. The genetic algorithm is used to specify the sequence of the examinations (or the courses) and then the heuristic algorithm spread them out into a timetable. An implementation (written in Python 3) provides a fast parallel processing calculation which can generate satisfactory schedules in tens of minutes. Performed experiments show approximately 13% better results in all considered criteria in comparison with utilized examination schedules in the past. The development was periodically consulted with persons responsible for the schedule processing at the faculty. The program will be used while designing of examination schedules for the academic year 2015/2016.
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Scenario generation by the moment fitting method
Koláčková, Hana ; Dupačová, Jitka (advisor) ; Branda, Martin (referee)
The thesis presents four methods for scenario generating leading to the resulting discrete probability distribution that replicates given values of the moments. The first method uses heuristic algorithm, the second method generates by symmetrically distributing values around the mean value, the third one is based on solving the system of nonlinear equations and finally the last method is based on goal programming. Next section describes the nature of problems solved by the goal programming. It also details possible ways of parameter specification to allow control of the computational complexity. In the last part of the thesis the results of several suitable methods for chosen types of problem are compared. Powered by TCPDF (www.tcpdf.org)
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The global optimalization methods
Dudová, Aneta ; Kozumplík, Jiří (referee) ; Mézl, Martin (advisor)
This bachelor work is dedicated to advanced methods of global optimization, and especially problem traveling salesman. It focuses on the description of the problem and its various options, including graph theory, heuristic algorithms, evolutionary algorithms, in which mainly genetic algorithms and optimization by ant colonies. In conclusion, the implementation of these methods and performed testing on different data sets of algorithms that approximately solve the traveling salesman problem.
|
|
|
Meta-Heuristic Solution in RCPSP
Šebek, Petr ; Kočí, Radek (referee) ; Hrubý, Martin (advisor)
This thesis deals with the description of the state of resource-constrained project scheduling problem. It defines the formal problem and its complexity. It also describes variants of this problem. Algorithms for solving RCPSP are presented. Heuristic genetic algorithm GARTH is analyzed in depth. The implementation of prototypes solving RCPSP using GARTH is outlined. Several improvements to the original algorithm are designed and evaluated.
|
|
|
System for Advanced Scheduling
Horký, Aleš ; Jaroš, Jiří (referee) ; Drahošová, Michaela (advisor)
This master thesis deals with the automatic design of examinations and courses scheduling. The design is adapted to the specific requirements of the Faculty of Information Technology of Brno University of Technology. A genetic algorithm and a heuristic algorithm are employed to solve this task. The genetic algorithm is used to specify the sequence of the examinations (or the courses) and then the heuristic algorithm spread them out into a timetable. An implementation (written in Python 3) provides a fast parallel processing calculation which can generate satisfactory schedules in tens of minutes. Performed experiments show approximately 13% better results in all considered criteria in comparison with utilized examination schedules in the past. The development was periodically consulted with persons responsible for the schedule processing at the faculty. The program will be used while designing of examination schedules for the academic year 2015/2016.
|
|
|
Heuristic Algorithms in Optimization
Komínek, Jan ; Šeda, Miloš (referee) ; Roupec, Jan (advisor)
This diploma thesis deals with genetic algorithms and their properties. Particular emphasis is placed on finding the influence of mutation and population size. Genetic algorithms are applied on inverse heat conduction problems (IHCP) in the second part of the thesis. Several different approaches and coding methods were tested. Properties of genetic algorithms were improved by definition of two new genetic operators – manipulation and sorting. Reported theoretical findings were tested on the real data of inverse heat conduction problem. The library for easy implementation of GA for solving general optimization problems in C ++ was created and is described in the last chapter.
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