Optimizing the Production Output Function in Dynamic Manufacturing Systems Using Genetic Algorithm

One of the main problems of the industrial society today is the low quality of products, the failure of products and systems, which causes problems at various levels of production and even causes catastrophic events for society and the environment. Weakness in dynamic manufacturing and production systems and weakness in optimizing these systems is an issue that needs to be addressed. Therefore, the main purpose of the present study is to answer some of these issues and problems in the statistical community. The researcher intends to analyze the issue as the main solution in the optimization of the output function of manufacturing systems. The problem was modeled by considering the constraints and assumptions set as nonlinear integer programming (MINLP). Then, to achieve the optimal global solution, using linearization techniques, the mathematical model of the problem was converted to linear integer linear programming (MILP). Based on this, the target function was examined using the genetic algorithm in MATLAB software and its results were presented for small, medium, and large dimensions for problem factories with different dimensions (sensitivity analysis).