Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Manufacturing flow line systems: a review of models and analytical results
Queueing Systems: Theory and Applications - Special issue on queueing models of manufacturing systems
Kriging interpolation in simulation: a survey
WSC '04 Proceedings of the 36th conference on Winter simulation
Kriging metamodeling in discrete-event simulation: an overview
WSC '05 Proceedings of the 37th conference on Winter simulation
A comprehensive review of methods for simulation output analysis
Proceedings of the 38th conference on Winter simulation
State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments
INFORMS Journal on Computing
Queueing-model based analysis of assembly lines with finite buffers and general service times
Computers and Operations Research
Kriging metamodeling in constrained simulation optimization: an explorative study
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Real-time prediction of order flowtimes using support vector regression
Computers and Operations Research
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Tradeoffs in building a generic supply chain simulation capability
Proceedings of the 40th Conference on Winter Simulation
IEEE Transactions on Evolutionary Computation
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
Genetic programming for attribute construction in data mining
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
A simple powerful constraint for genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Computers and Operations Research
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In this article, an empirical analysis of experimental design approaches in simulation-based metamodelling of manufacturing systems with genetic programming (GP) is presented. An advantage of using GP is that prior assumptions on the structure of the metamodels are not required. On the other hand, having an unknown structure necessitates an analysis of the experimental design techniques used to sample the problem domain and capture its characteristics. Therefore, the study presents an empirical analysis of experimental design methods while developing GP metamodels to predict throughput rates in a common industrial system, serial production lines. The objective is to identify a robust sampling approach suitable for GP in simulation-based metamodelling. Experiments on different sizes of production lines are presented to demonstrate the effects of the experimental designs on the complexity and quality of approximations as well as their variance. The analysis showed that GP delivered system-wide metamodels with good predictive characteristics even with the limited sample data.