Optimization of production scheduling using machine learning algorithms in a manufacturing environment

Thesis Abstract

Abstract
This thesis presents a comprehensive study on the optimization of production scheduling in a manufacturing environment through the utilization of machine learning algorithms. The increasing complexity and dynamic nature of modern manufacturing operations necessitate the development of advanced scheduling techniques to improve efficiency, reduce production costs, and enhance overall productivity. Machine learning algorithms offer promising solutions to address these challenges by enabling automated decision-making based on historical data and real-time information. Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, research objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the foundation for the subsequent chapters by establishing the context and rationale for the research. Chapter 2 consists of a comprehensive literature review that explores existing studies, methodologies, and technologies related to production scheduling optimization and machine learning algorithms. The review covers various aspects such as traditional scheduling methods, challenges in production scheduling, applications of machine learning in manufacturing, and recent advancements in the field. Chapter 3 details the research methodology employed in this study, including data collection methods, selection of machine learning algorithms, model development, validation techniques, and experimental design. The chapter provides a detailed overview of the research approach and methodology used to achieve the research objectives. Chapter 4 presents a thorough discussion of the findings obtained from the application of machine learning algorithms to optimize production scheduling in a manufacturing environment. The chapter analyzes the results, interprets the implications of the findings, and discusses the practical implications for industry stakeholders. Chapter 5 serves as the conclusion and summary of the thesis, summarizing the key findings, implications, limitations, and future research directions. The chapter concludes with recommendations for the implementation of optimized production scheduling using machine learning algorithms in manufacturing settings. Overall, this thesis contributes to the body of knowledge in the field of industrial and production engineering by demonstrating the potential of machine learning algorithms in enhancing production scheduling efficiency and performance in manufacturing environments. The findings of this research have practical implications for industry practitioners seeking to improve operational efficiency, reduce costs, and maximize productivity through advanced scheduling techniques.