SIMULATION-BASED OPTIMIZATION OF PRODUCT PRIORITIZATION IN A MANUFACTURING FLOWSHOP AT GKN AEROSPACE

GKN Aerospace in Trollhättan manufactures different components for aircraft engines and aero derivative gas turbines. A new workshop has recently been installed that is highly automated and includes operations such as laser welding, x-ray and burring. Their production flow is mostly based on first in first out rules except for the x-ray stations where the workers can select which part they want to begin with. Currently, the workers select the parts based on experience which sometimes is not the most optimal solution. Therefore, they want to improve their selection process in order to reduce delays in their production which is why a priority system is required. Simulation based optimization will be used in order to find near optimal priority lists. Experts at GKN Aerospace have created a discrete event simulation model over their workshop using SIMUL8. In order to find near optimal priority lists for the workshop a new optimization program needs to be created that is compatible with SIMUL8. The program will need to fulfill some requirements to both work as an optimization program and also to be used by the workers at GKN Aerospace. Since multi objective evolutionary algorithms have the advantage of exploring the objective space, this type of method is utilized. The objective is to minimize the total delay in the system which makes this a single-objective optimization problem but since the simulation model contains stochastic behavior the result will differ between each run and therefore several replications is needed. With several replications more outputs can be read, in this case mean value and standard deviation will make this a multi objective optimization problem. The question that is investigated throughout this thesis is: Can a multi-objective evolutionary algorithm efficiently find a robust solution with low delays by considering both mean value and standard deviation as objectives? The aim of this project is to create a priority system that uses an optimization algorithm which together with the simulation model can optimize the production at GKN Aerospace. The purpose of the optimization algorithm is to find robust solutions with low delays in order to improve the production at GKN Aerospace. With the priority system GKN Aerospace may improve their process by following priority lists which has been optimized and therefore reduce waste in form of delays. With the aim of reducing both standard deviation and mean value, a robust solution may be found which means that a small change in the production will not disturb the outcome of the workshop. During this project a priority system was created that considers both the developer and the workers at GKN Aerospace. The priority system is web based where the workers uses a web browser to access the priority list while the developer uses an experiment system in order to improve the underlying optimization algorithm. With the experiment system the non-dominated sorting genetic algorithm II was implemented in order to solve the real case problem