Design of grid systems to solve complex industrial engineering problems


Год издания: 2017

Идентификатор DOI: 10.1109/ICIEAM.2016.7911574

Ключевые слова: decision support, genetic algorithm, grid, modelling, optimization, Artificial intelligence, Complex networks, Decision making, Decision support systems, Genetic algorithms, Manufacture, Models, Neural networks, Personal computers, Production, Computational resources, Decision supports, Multi-objective genetic algorithm, Optimization problems, Performance and reliabilities, Practical problems, System efficiency, Problem solving

Аннотация: The problem of solving complex and demanding tasks of production engineering is discussed. The complexity of the methods used in the solution of such tasks requires significant computational resources. It is proposed to use the widespread personal computers combined into a single computer network on the basis of the Grid technologyПоказать полностью. However, the informal aspect is the choice of an effective structure of such a system. A complex of mathematical models for assessing performance and reliability of Grid systems and the corresponding problem of selecting its effective structure is proposed. On the basis of the proposed models the decision support system for the Grid systems formation is developed. To solve optimization problems arising in the process of decision-making, the multiobjective genetic algorithm is offered. When studying the proposed system efficiency, a practical problem of designing effective grid system configuration was solved, which is focused on the complex task of network planning by means of artificial neural networks. © 2016 IEEE.

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Журнал: 2016 2nd International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2016 - Proceedings


  • Tynchenko V.V. (Dept. of Informatics, Siberian Federal University, Krasnoyarsk, Russian Federation)
  • Solovyov E.A. (Dept. of Production Machinery and Equipment for Petroleum and Natural Gas Engineering, Siberian Federal University, Krasnoyarsk, Russian Federation)
  • Tynchenko S.V. (Dept. of Economics and Information Technologies of Management, Siberian Federal University, Krasnoyarsk, Russian Federation)