Genetic Algorithms For Flow-shop Scheduling Optimization Of An Automated Assembly Line
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Manufacturing process is a process of producing and creating a product with the use of technologies and machinery resources. In manufacturing process there are three dimensions, which are important in improving the system. These are cost, quality, and speed that can be considered as basics of every process. In this thesis speed of the manufacturing process is enhanced, which leads to reduction in cost as well. Assembly lines are the part of manufacturing process to convert raw materials into finished products. Considering optimization problems in assembly lines, applying genetic algorithms to the established model could lead to efficient manufacturing. Genetic algorithm is a programming search technique for maximizing productivity, minimizing inefficiency and reducing production time. This work presents an approach for developing simulation models used for optimization of production lines. The results are demonstrated using the assembly line which is located in FAST-Lab. at Tampere University of Technology. The simulation of the line is created to assess cycle times and utilization of workstations using MATLAB and SimEvents library. The optimization, in the context of presented work, is the process of locating and scheduling the products in the line achieving best timing to fulfil production orders. The workstations can be first balanced for better performance and then products are scheduled based on reduction of the production time.