Features of implementation of simulation processes based on DATA FARMING methodologies

E.A. Krikovlyuk, V.A. Pepelyaev, M.A. Sahnyuk

Abstract


The basic concepts and features of application of methodology Data Farming in simulation practice of complex stochastic systems are considered. There is proposed based on specified concepts the approach to increase efficiency of distributed simulation system NEDISOPT_D developed in V. M. Glushkov Institute of cybernetics.

Keywords


simulation modeling

Full Text:

PDF (Ukrainian)

References


Fu M. Optimization for Simulation: Theory and Practice // INFORMS J. on Computing. – 2002. – № 14(3). – P. 192 – 215.

April J., Glover F., Kelly J.P., Laguna M. Practical introduction to simulation optimization // Proc. of the 2003 Winter Simul. Conf. – 2003. – P. 71 – 78.

Horne G. E., Meyer T. E. Data Farming: Discovering Surprise // Proc. of the Winter Simulation Conf., 2005. – P. 1082 – 1087.

SEED Center for Data Farming // http://harvest.nps.edu/

Галаган Т.Н., Пепеляев В.А., Сахнюк М.А. Особенности реализации многослойного сценария распределенного поиска оптимальных решений // Проблеми програмування. – 2008. – № 2 - 3. – С. 636 – 640.

Barry Ph, Koehler M. Simulation in context: using Data Farming for decision Support // Proc. of the Winter Simulation Conf., 2004. – P. 814 – 819.

Horne G.E., Schwierz K.-P. Data Farming around the world overview // Proc. of the Winter Simulation Conf., 2008. – P. 1442 – 1447.

Choo C.S., Ng E.C., Ang D., Chua C.L. Data Farming: a brief history // Proc. of the Winter Simulation Conf., 2008. – P. 1448 – 1455.

Пепеляев В.А., Сахнюк М.А., Чёрный Ю.М. Параллельная реализация процессов направленного поиска оптимальных решений // Проблеми програмування. – 2010. – № 2 – 3. – С. 572 – 576.


Refbacks

  • There are currently no refbacks.