Explainable and Customizable Job Sequencing and Scheduling Advancing Production Control and Management with XAI

This book systematically reviews the progress in explainable AI (XAI) and introduces the methods, tools, and applications of XAI technologies in job sequencing and scheduling. Relevant references and real case studies are provided as supporting evidence.

To date, artificial intelligence (AI) technologies have been widely applied in job sequencing and scheduling. However, some advanced AI methods are not easy to understand or communicate, especially for factory workers with insufficient background knowledge of AI. This undoubtedly limits the practicability of these methods. To address this issue, explainable AI has been considered a viable strategy. XAI methods suitable for job sequencing and scheduling differ from those for other fields in manufacturing, such as pattern recognition, defect analysis, estimation, and prediction. This is the first book to systematically integrate current knowledge in XAI and demonstrate its application to manufacturing.

Chapter 1. Explainable Artificial Intelligence (XAI).- Chapter 2. Artificial Intelligence (AI) Applications in Job Sequencing and Scheduling.- Chapter 3. XAI Applications to Job sequencing and Scheduling.- Chapter 4. Explaining Artificial Neural Network and Deep Learning Applications in Job Sequencing and Scheduling.- Chapter 5. Explaining Genetic Algorithm and Other Bio-inspired Algorithm Applications in Job Sequencing and Scheduling.- Chapter 6. Tailoring Scheduling Rules Using XAI.- Chapter 7. XAI-enabled Edge Computing Application in Job Sequencing and Scheduling.

Dr. Chen has published papers on job scheduling in semiconductor manufacturing factories in journals such as Expert Systems and Applications, Neural Computing and Applications, International Journal of Advanced Manufacturing Technology, Journal of Intelligent Manufacturing, and Computers and Industrial Engineering. He also authored a book entitled "Production Planning and Control in Semiconductor Manufacturing: Big Data Analytics and Industry 4.0 Applications" for Springer in 2022. He has guest edited several special issues on job scheduling in large-scale manufacturing systems and just-in-time manufacturing and maintenance for journals including Operations Research, International Journal of Intelligent Systems, and International Journal of Advanced Manufacturing Technology. In 2023, he authored a book entitled "Explainable Artificial Intelligence in Manufacturing: Methodology, Tools, and Applications" for Springer.