Thesis Abstract

Abstract
This thesis focuses on the application of Artificial Intelligence (AI) techniques to optimize production processes within a manufacturing environment. The integration of AI technologies has become increasingly important in modern industrial settings to enhance efficiency, productivity, and overall performance. This study aims to explore the potential benefits and challenges associated with implementing AI-driven optimization strategies in manufacturing operations. The introduction provides an overview of the significance of AI in industrial and production engineering, emphasizing its role in revolutionizing traditional manufacturing processes. The background of the study delves into the current state of production processes and the need for innovative solutions to address inefficiencies and bottlenecks. The problem statement highlights the specific challenges faced by manufacturing industries in optimizing their production processes and the potential of AI to address these issues. The objectives of the study are outlined to guide the research towards achieving specific goals, such as improving production efficiency, reducing costs, and enhancing overall quality. The limitations of the study are acknowledged to provide a realistic perspective on the scope and constraints of the research. The scope of the study defines the boundaries within which the research will be conducted, focusing on a specific segment of the manufacturing industry. The significance of the study is emphasized in terms of its potential impact on industrial practices, technological advancements, and the overall competitiveness of manufacturing operations. The structure of the thesis is outlined to provide a roadmap for the reader, detailing the organization of chapters and key components of the research. Definitions of key terms used throughout the thesis are provided to ensure clarity and understanding of concepts. The literature review explores existing research and case studies related to AI applications in manufacturing optimization. Ten key themes are identified and analyzed to provide a comprehensive overview of the current state of the field. The research methodology outlines the approach, methods, and tools used to collect and analyze data, including case studies, simulations, and empirical studies. The discussion of findings presents the results of the research, including insights, trends, challenges, and opportunities identified through data analysis. The implications of the findings are discussed in relation to theoretical frameworks, practical applications, and future research directions. The conclusion summarizes the key findings, contributions, and recommendations of the study, highlighting the potential impact of AI-driven optimization in manufacturing environments. In conclusion, this thesis contributes to the growing body of knowledge on the application of AI in production processes and provides valuable insights for industry practitioners, researchers, and policymakers. The findings offer practical recommendations for implementing AI-driven optimization strategies in manufacturing operations, paving the way for enhanced efficiency, productivity, and competitiveness in the industry.

Thesis Overview