Explainable and Customizable Job Sequencing and Scheduling
Language: English
Published by Springer Verlag GmbH, 2025
Series: Book 405 of 472 - SpringerBriefs in Applied Sciences and Technology
- Softcover
- New
Seller: moluna, Greven, Germanymoluna
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- Title
- Explainable and Customizable Job Sequencing and Scheduling
- Author
- Chen, Tin-Chih Toly
- Publisher
- Springer Verlag GmbH
- Publication year
- 2025
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 3031853733
- ISBN 13
- 9783031853739
- Series
- Book 405 of 472: SpringerBriefs in Applied Sciences and Technology
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.
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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