Capacity improvement in a real manufacturing system using a hybrid simulation/genetic algorithm approach

The primary aim of this M.Sc study is to suggest simulation and genetic algorithm based hybrid approach for capacity improvement in a real manufacturing system. In the first phase of the study, a detailed simulation model of the manufacturing system studied is developed using simulation language, Arena 10.0. Following, the verification and the validation of the model developed, potential bottleneck machines in this system are identified using this simulation model. In the following phase, the suggested hybrid method which combines genetic algorithm and simulation is employed to allocate buffers so that the capacity of the production line can be improved.