Application of the Holonic Manufacturing System using the Genetic Algorithm : Case Study in Lab 7 of the General Company for the Leather Industry - سلطان الطائي يوسف حجيم; مولود البرزنجي سهى جمال (Journal of Economics and Administrative Sciences, 2019)
The study aims to achieve several objectives, including follow-up scientific developments and transformations in the modern concepts of the Holistic Manufacturing System for the purpose of identifying the methods of switching to the entrances of artificial intelligence, and clarifying the mechanism of operation of the genetic algorithm under the Holonic Manufacturing System, to benefit from the advantages of systems and to achieve the maximum savings in time and cost of machines Using the Holistic Manufacturing System method and the Genetic algorithm, which allows for optimal maintenance time and minimizing the total cost, which in turn enables the workers of these machines to control the vacations in them, and based on the intellectual dilemma and to the problem of the field can be asked the question how to achieve the Holistic Manufacturing System using the genetic algorithm ?, and in light of which the importance of the study and its objectives were based on the analytical descriptive method in the theoretical framework. In the practical framework was based on the quantitative approach that used quantitative indicators of the separation line Comparing the results between the HOL method and the genetic algorithm method. After evaluating and testing the data, the data were analyzed using indicators. The results showed that the use of the genetic algorithm helped to reduce the effort, time and cost. It is possible to reach the optimal solution with very few steps when using the genetic algorithm as a random search algorithm. The main recommendations were the adoption of the laboratory management on the genetic algorithm. And the completion of the study with some proposals, the most prominent of which is to conduct further research on the Holistic Manufacturing System using the genetic algorithm and in various industrial and service sectors.