Optimization of Production Scheduling in a Manufacturing Plant Using Artificial Intelligence

Thesis Abstract

Abstract
This thesis presents a comprehensive study on the optimization of production scheduling in a manufacturing plant using artificial intelligence (AI) techniques. The manufacturing industry is constantly seeking ways to improve efficiency, reduce costs, and enhance productivity. One crucial aspect of manufacturing operations is production scheduling, which involves determining the most efficient sequence of tasks and allocating resources to meet production demands. Traditional production scheduling methods often fall short in handling the complexity and dynamic nature of modern manufacturing environments. In this study, AI technologies, specifically machine learning algorithms and optimization techniques, are leveraged to develop a novel approach to production scheduling that can adapt to changing conditions in real-time. The research begins with a thorough introduction (Chapter 1) that sets the stage for the study. It includes the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. The literature review (Chapter 2) delves into existing research on production scheduling, AI applications in manufacturing, and related optimization techniques. This chapter provides a solid foundation for understanding the current state of the art and identifying gaps in the literature that this study aims to address. Chapter 3 focuses on the research methodology, detailing the approach taken to develop and implement the AI-based production scheduling system. Key components of this chapter include data collection methods, algorithm selection, model development, validation techniques, and performance evaluation metrics. The research methodology is crucial in ensuring the rigor and reliability of the study findings. In Chapter 4, the discussion of findings presents the results of applying the AI-based production scheduling system in a real manufacturing plant setting. The effectiveness and efficiency of the proposed approach are evaluated based on key performance indicators such as lead time reduction, resource utilization improvement, and overall production throughput enhancement. The findings are analyzed in detail, highlighting the strengths and limitations of the AI system and comparing its performance against traditional scheduling methods. Finally, Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and offering recommendations for future work. The conclusion emphasizes the significance of AI technologies in revolutionizing production scheduling practices and underscores the potential benefits for the manufacturing industry. Overall, this study contributes to advancing the field of industrial and production engineering by demonstrating the feasibility and effectiveness of using AI for optimizing production scheduling processes.