Thesis Abstract

Abstract
The efficient scheduling of production activities is crucial for enhancing productivity and reducing operational costs in manufacturing facilities. This thesis explores the optimization of production scheduling in a manufacturing facility through the implementation of advanced algorithms. The study focuses on developing a scheduling model that utilizes advanced algorithms to improve the allocation of resources, minimize production lead times, and enhance overall production efficiency. The research begins by providing an overview of the current challenges faced in production scheduling and the significance of implementing advanced algorithms to address these challenges. A comprehensive literature review is conducted to explore existing scheduling models, algorithms, and optimization techniques used in manufacturing environments. This review highlights the gaps in current practices and emphasizes the need for more advanced approaches to production scheduling. The methodology chapter details the research design and approach adopted for this study, including data collection methods, algorithm selection criteria, and model development processes. The research methodology involves collecting production data from a real manufacturing facility, analyzing the data to identify bottlenecks and inefficiencies in the current scheduling process, and implementing advanced algorithms to optimize the production schedule. The findings chapter presents the results of the study, including the performance improvements achieved through the implementation of advanced algorithms in the production scheduling process. The findings highlight the benefits of using advanced algorithms, such as reduced lead times, improved resource utilization, and increased production throughput. The discussion section provides a detailed analysis of the findings and their implications for manufacturing facilities seeking to enhance their production scheduling processes. In conclusion, this thesis demonstrates the effectiveness of utilizing advanced algorithms for optimizing production scheduling in manufacturing facilities. The study contributes to the existing body of knowledge by showcasing the practical application of advanced algorithms in addressing the challenges associated with production scheduling. The findings of this research provide valuable insights for manufacturing managers and practitioners looking to improve their production scheduling processes and enhance operational efficiency.

Thesis Overview