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Planning and Scheduling
Hefka, Lukáš ; Kočí, Radek (referee) ; Janoušek, Vladimír (advisor)
This thesis deals with optimization problems of planning and scheduling. There are using genetic algorithms which are inspired by evolution process. Main work is familiar with the problem of planning and scheduling, genetic algorithm and Petri nets. This knowledge was used to create applications that would with the use of genetic algorithms was able to solve planning problems and the resulting plans would be represented the Time Petri Net. In conclusion of the this thesis are presented obtained results and examples of field use.
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Process Control with Dynamic Resource Scheduling
Šinkora, Jan ; Kočí, Radek (referee) ; Janoušek, Vladimír (advisor)
This project pursues issues on the border of information technologies and process optimization. Previously published concepts of~modeling projects and shared resources with object-oriented Petri nets are presented and further expanded. The possibilites of~the use of~genetic algorithms for dynamic realtime optimization of the resource schedules are explored. The resource constrained project sheduling problem is presented and it is shown, how instances of the problem can be implemented. A more complex model that is inspired by real production systems is then created. Next, a control agent, which monitors a running production system and allows for it's dynamic optimization is designed. The whole system is implemented in the Squeak Smalltalk environment with the use of the tool PNtalk, which is an experimental implementation of the object oriented Petri nets paradigm.
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Real-Time Optimizations in Industrial Production
Křen, Michal ; Kočí, Radek (referee) ; Hrubý, Martin (advisor)
The thesis deals with the scheduling problem of manufacturing operations in industrial production. This problem is described as the well-known the Resource-Constrained Project Scheduling Problem. The objective of this problem is to find an optimal assignment of operations to limited resources. Optimizer created for the thesis uses a genetic algorithm to solve the scheduling problem. For the purpose of a dynamic scheduling, a failures model was designed and a system with real-time optimizer, that is able to repair the original schedule fluently, was created. In the real-time optimizer, several solution methods were implemented and these solution methods underwent a number of experiments. The system thus created is also able to simulate manufacturing operations and draw a Gantt chart.
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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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APS system application for planning and scheduling
Zápotočný, Václav ; Hromková, Ivana (referee) ; Simeonov, Simeon (advisor)
Master's thesis, made at the Institute of Production Machines, Systems and Robotics, aims, evaluate existing production of slope mowers manufactured by Dvořák – svahové sekačky s.r.o., and then perform data collection for obtaining complete technological processes of production. With the APS (Advanced Planning and Scheduling) system, simulate and design an optimized solution, with a view to significantly reduce the required storage current inventory of manufactured parts for assembly of that slope mower.
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Genetic Algorithms and Scheduling
Škrabal, Ondřej ; Popela, Pavel (referee) ; Roupec, Jan (advisor)
This work deals with scheduling problem in particular plastic production service. The solution is based on heuristic algorithms, programming languages C + +, C # and is built on the .NET framework and LINQ to XML. It provides the users with comparisons of the heuristic approach with genetic algorithms applied to production problem. All methods results are compared in relation to hand-arranged plans.
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Application of genetic algorithm for production scheduling of engineering company
Stariat, Jiří ; Skočdopolová, Veronika (advisor) ; Zouhar, Jan (referee)
This thesis is engaged in scheduling problem, his special types and methods of solving. Scheduling problem is a common operations research problem, which ranks among combinatorial problems. The aim of the scheduling problem is to assign certain activities and resources to individual time moments. Scheduling problem is NP-complete problem. Its computational complexity is thus so high, that there is currently no known algorithm that precisely solve its any instance in polynomial time. Is therefore used for its solution heuristics and metaheuristcs. In this thesis is described in detail metaheuristics of genetic algorithm. Application of genetic algorithm for production scheduling of specific engineering company is the main objective of this thesis.
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University Schedule Optimisation
Skrbková, Tereza ; Jablonský, Josef (advisor) ; Skočdopolová, Veronika (referee)
Scheduling is a practical optimisiation problem which can be solved by means of integer or binary programming methods. This paper focuses on university scheduling, in particular the schedule of the University of Economics in Prague, it is however possible to apply the results to schedules of other universities. We begin with the basics of linear programming, focusing on integer and binary programming as well as selected methods used to solve these problems. We then construct an optimisation model for the schedule of a subset of subjects based on real requirements (data 2009) and we compare the results with the actual schedule of the University of Economics in Prague. In conclusion we discuss some of the assumptions made during model development. The model is then generalised to include the entire set of subjects of the university. For the conversion of the software results into a more legible format, we include two MS Excel macros as part of this paper.
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