Performance Analysis of Flowshop Scheduling Using Genetic Algorithm Enhanced with Simulation

This paper uses the genetic algorithm combined with simulation techniques to evaluate the performance of flowshop scheduling. The problem of scheduling is a NP combinatorial optimization problem. The objective of the study is to develop robust scheduling by using Genetic Algorithms (GA) to obtain good solutions may be close to optimum sequence with minimum cycle time or make span time for a flow shop problem. The next step is to use simulation to analyze the performance of selected sequences to achieve better resource utilization. In this research, newly hybrid genetic algorithm is developed using C-programming language for four different methods to solve flow shop scheduling problems. The proposed methods are implemented on a number of tested problems and compared with exact solutions on smaller scale problems. The alternative sequence obtained here is further analyzed by simulating the production model using ARENA software