Thesis Abstract

Abstract
The manufacturing industry is constantly evolving with the rapid advancement of technology. In this context, the optimization of production scheduling using Artificial Intelligence (AI) techniques has garnered significant attention as a means to enhance efficiency and productivity in manufacturing environments. This thesis explores the application of AI techniques to optimize production scheduling within a manufacturing setting, aiming to address the complexities and challenges associated with traditional scheduling methods. The primary objective of this research is to develop and implement an AI-based production scheduling system that can effectively optimize resource utilization, minimize production lead times, and improve overall operational performance. The research methodology employed in this study involves a comprehensive literature review to establish a theoretical foundation for AI techniques in production scheduling. Subsequently, a detailed analysis of existing scheduling methods and AI algorithms is conducted to identify the most suitable approaches for implementation. The research methodology also includes the development of a simulation model to evaluate the performance of the proposed AI-based production scheduling system in a controlled environment. The findings of this study indicate that AI techniques, such as machine learning algorithms and optimization models, offer significant advantages in optimizing production scheduling processes. By leveraging historical production data and real-time information, AI algorithms can generate schedules that adapt dynamically to changing production requirements and constraints. The simulation results demonstrate that the AI-based production scheduling system outperforms traditional scheduling methods in terms of efficiency, accuracy, and responsiveness to production disruptions. The discussion of findings delves into the practical implications of implementing an AI-based production scheduling system in a manufacturing environment. Key considerations such as data requirements, algorithm selection, system integration, and scalability are examined to provide insights into the implementation challenges and considerations for industrial applications. The implications of this research extend to various industries seeking to enhance their production scheduling processes through the adoption of AI technologies. In conclusion, this thesis contributes to the existing body of knowledge on production scheduling optimization by demonstrating the efficacy of AI techniques in improving manufacturing operations. The successful implementation of an AI-based production scheduling system offers tangible benefits in terms of cost reduction, resource optimization, and enhanced operational efficiency. The insights gained from this research provide a valuable roadmap for manufacturing organizations looking to leverage AI technologies for competitive advantage in a dynamic and competitive market landscape.

Thesis Overview