Complexity reduction in fuzzy modeling
Selected papers from the 2nd IMACS symposium on Mathematical modelling---2nd MATHMOD
Model abstraction for discrete event systems using neural networks and sensitivity information
Proceedings of the 32nd conference on Winter simulation
Theory of Modeling and Simulation
Theory of Modeling and Simulation
Simulation Modeling Handbook: A Practical Approach
Simulation Modeling Handbook: A Practical Approach
Fuzzy modeling of manufacturing and logistic systems
Mathematics and Computers in Simulation
Simulation: The Practice of Model Development and Use
Simulation: The Practice of Model Development and Use
New manufacturing modeling methodology: a hybrid approach to manufacturing enterprise simulation
Proceedings of the 35th conference on Winter simulation: driving innovation
Proceedings of the 38th conference on Winter simulation
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The objective of the paper is to present the concept of using selected computational intelligence methods in conjunction with discrete event simulation (DES) models of chosen logistics processes. A review of the recent literature in the scope of applications of discrete event simulation methods indicates that researchers who use these methods more and more often employ techniques from the area of computational intelligence, especially in cases when the phenomena, processes or systems modeled feature complexity, uncertainty or non-linearity. The issues discussed in the paper refer to modeling selected logistics processes at the company that produces electricity and thermal energy.