Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Goal driven simulation intelligent back ends: a state of the art review
WSC '96 Proceedings of the 28th conference on Winter simulation
Simulation optimization: methods and applications
Proceedings of the 29th conference on Winter simulation
Belief networks in construction simulation
Proceedings of the 30th conference on Winter simulation
Simulation optimization methodologies
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Simulation optimization with the linear move and exchange move optimization algorithm
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Simulation optimization: a survey of simulation optimization techniques and procedures
Proceedings of the 32nd conference on Winter simulation
Proceedings of the 33nd conference on Winter simulation
Panel: future of simulation: panel session: the future of simulation
Proceedings of the 33nd conference on Winter simulation
Simulation response optimization via direct conjugate direction method
Computers and Operations Research
Efficient simulation-based discrete optimization
WSC '04 Proceedings of the 36th conference on Winter simulation
A GA-based parameter design for single machine turning process with high-volume production
Computers and Industrial Engineering
International Journal of Computer Applications in Technology
An alternating variable method with varying replications for simulation response optimization
Computers & Mathematics with Applications
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Advances have been made in optimizing quantitative variables within a simulation model, and many methodologies now exist for this purpose. However, many of the design decisions which confront a system's users involve policy alternatives. Often, variables used to represent these alternatives are not only discrete but qualitative. This work seeks to develop a simulation-optimization methodology which can operate on qualitative variables. The proposed approach is to link a genetic algorithm with an object-oriented simulation model generator. The system designs recommended by the genetic algorithm are converted to simulation models and executed. The results then guide the genetic algorithm in its selection of future designs. A simulation model generator for a class of manufacturing systems and a genetic algorithm which can interface with the generator have been developed. The methodology has shown positive results.