The role of work-in-process inventory in serial production lines
Operations Research
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Buffer allocation in flow-shop-type production systems with general arrival and service patterns
Computers and Operations Research
Preventing overfitting in GP with canary functions
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Kriging interpolation in simulation: a survey
WSC '04 Proceedings of the 36th conference on Winter simulation
Simulation-based optimization for repairable systems using particle swarm algorithm
WSC '05 Proceedings of the 37th conference on Winter simulation
Grid enabled sequential design and adaptive metamodeling
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
A review of particle swarm optimization. Part I: background and development
Natural Computing: an international journal
An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers
Computers and Operations Research
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A discrete particle swarm optimization algorithm for scheduling parallel machines
Computers and Industrial Engineering
A comparative study of genetic algorithm components in simulation-based optimisation
Proceedings of the 40th Conference on Winter Simulation
A simple powerful constraint for genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Additive sequential evolutionary design of experiments
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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This article presents an application of two main component methodologies of evolutionary algorithms in simulation-based metamodelling. We present an evolutionary framework for constructing analytical metamodels and apply it to simulations of manufacturing lines with buffer allocation problem. In this framework, a particle swarm algorithm is integrated to genetic programming to perform symbolic regression of the problem. The sampling data is sequentially generated by the particle swarm algorithm, while genetic programming evolves symbolic functions of the domain. The results are promising in terms of efficiency in design of experiments and accuracy in global metamodelling.