Optimization of operator allocation in a large multi product assembly shop through unique integration of simulation and genetic algorithm

This study presents an integrated simulation and genetic algorithm (GA) for optimum operator allocation in a large multi product assembly shop. At first, simulation is used as an exquisite tool for modeling and analyzing the true performance of the system. Then, GA is used to maximize throughput of the system. In other words, optimal number of operators is found using GA such that the throughput is maximized. It is shown that the integrated GA-simulation approach yields considerable savings and benefits. The focus of the GA-simulation approach is on complex problem settings where there is random stochastic variability in the modeling environment. The results of this study show that the integrated GA-simulation is ideal for problems with several numbers of parameters and variables, and complex objective function. This is the first study that integrates GA and simulation for optimum allocation of operators in multi product assembly shops.