Optimization of Production Line Layout using Simulation and Genetic Algorithm in a Manufacturing Plant

Thesis Abstract

Abstract
In the realm of industrial and production engineering, the optimization of production line layouts plays a pivotal role in enhancing efficiency and productivity within manufacturing plants. This thesis delves into the application of simulation and genetic algorithms to optimize production line layouts in a manufacturing plant setting. The study aims to address the challenges faced in traditional production line layouts by leveraging advanced computational techniques to achieve optimal configurations. The thesis begins with a comprehensive introduction that sets the stage for the research. It explores the background of the study, highlighting the significance of production line layout optimization in the context of industrial engineering. The problem statement identifies the existing inefficiencies in production line layouts that necessitate a more systematic approach. The objectives of the study outline the specific goals and targets to be achieved through the optimization process. Acknowledging the limitations of the study, the research delineates the constraints and boundaries within which the optimization process will be conducted. The scope of the study defines the extent of the investigation, including the specific aspects of production line layout design that will be examined. The significance of the study underscores the potential impact of optimized production line layouts on overall manufacturing plant performance. The structure of the thesis provides an overview of the organization of the subsequent chapters, guiding the reader through the research journey. Definitions of key terms ensure clarity and understanding of the terminology used throughout the thesis. The literature review in Chapter Two delves into existing research and studies related to production line layout optimization, simulation techniques, and genetic algorithms. Ten distinct themes are explored, providing a thorough foundation for the research methodology. Chapter Three details the research methodology employed in this study. From problem formulation to data collection and analysis, each step is meticulously described. The use of simulation tools and genetic algorithms is elaborated upon, outlining how these techniques will be integrated into the optimization process. The chapter also addresses ethical considerations and the validity of the methodology. Chapter Four presents the findings of the research, showcasing the optimized production line layouts generated through simulation and genetic algorithms. The discussion delves into the implications of these findings on manufacturing plant efficiency, highlighting the benefits of adopting optimized layouts. Finally, Chapter Five offers a conclusion and summary of the project thesis. The key findings, contributions, and limitations of the study are reiterated, providing a comprehensive overview of the research outcomes. Recommendations for future research and practical implications for industrial applications are also discussed. In conclusion, the optimization of production line layouts using simulation and genetic algorithms offers a promising avenue for enhancing efficiency and productivity in manufacturing plants. This thesis contributes to the body of knowledge in industrial and production engineering, presenting a systematic approach to achieving optimal production line configurations.