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Tytuł artykułu

The use of simulation and genetic algorithm with different genetic operators to optimize manufacturing system

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article depicts an evolutionary approach to simulation based optimization of a typical manufacturing system. Genetic algorithm with four different variants of genetic operators (crossover operator and type of selection) is compared to find the best optimization method. A comprehensive discussion of the genetic algorithm results obtained from the simulation model was also presented.
Rocznik
Strony
34--45
Opis fizyczny
Bibliogr. 12 poz., fig.
Twórcy
  • West Pomeranian University of Technology, Szczecin, Department Of Information Systems Engineering, 71-210 Szczecin, Żołnierska 49
  • West Pomeranian University of Technology, Szczecin, Department Of Information Systems Engineering, 71-210 Szczecin, Żołnierska 49
Bibliografia
  • [1] Goldberg D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Boston, 1989.
  • [2] Scott L.R., Harmonosky C.M.: An improved simulated annealing simulation optimization method for discrete parameter stochastic systems. Computers & Operations Research, 32, 2005, p. 343–358.
  • [3] Banks J., Carson J.S., Nelson B.L., Nicol D.M.: Discrete-Event System Simulation. Prentice Hall Inc., New Jersey, 2001.
  • [4] Paul R.J., Chanev T.S.: Simulation optimisation using a genetic algorithm. Simulation Practice and Theory 6, 1998, p. 601–611.
  • [5] Azadeh A., Tarverdian S.: Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption. Energy Policy, 35, 2007, p. 5229–5241.
  • [6] Zhang H., Gen M.: Multistage-based genetic algorithm for flexible job shop scheduling problem. Complex Int., 11, 2005, p. 223–232.
  • [7] Entriken R., Vössner S.: Genetic Algorithms with cluster Analysis for Production Simulation. Proceedings of the 1997 Winter Simulation Conference, p. 1307 – 1314.
  • [8] Cakar T., Yildirim M.B.: A neuro-genetic approach to designand planning of a manufacturing cell. Journal of Intelligent Manufacturing, 16, 2005, p. 453–462.
  • [9] Can B., Beham A., Heavey C.: A Comparative study of Genetic Algorithm Components in Simulation-Based Optimisation. Proceedings of the 2008 Winter Simulation Conference, p. 1829-1837.
  • [10] Eskandari H., Rabelo L., Mollaghasemi M.: Multiobjective Simulation Optimization using an Enhanced Genetic Algorithm. Proceedings of the 2005 Winter Simulation Conference, p. 833-841.
  • [11] Konstam, A.H., Hartley, S.J., Carr, W.L.: Optimiaztion in a Distributed Proccesing Environment using Genetic Algorithm with Mutivariate Crossover. Proceeding of the 1992 ACM annual conference on Communications, p. 109-116.
  • [12] Holland J.K.: Adaptation in Neural and Artificial Systems. University of Michigan Press, Ann Arbor, MI., 1975.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-75363b2f-1aa4-49e4-911c-ad0cc86e6fbe
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