Efficiency improvement of job scheduling by using Genetic Algorithm: A case study in electronic industry

In this paper, we present the implementation of Genetic Algorithms (GA) which are modified to deal with the job scheduling in the electronic assembly industry. The performance comparison showed that the proposed GA gives perform significantly better in decreasing makespan and idle time. Furthermore, we accelerated the proposed GA by using the solution from the conventional heuristic methods as the initial population. It showed that the solution converges to the optimum faster than the former. However, due to the nature of stochastic search conducted by GA, we also focus on GA parameters which through experiment design and fine tuning of parameters.