Introduction to Mathematical Models in Operations Planning
Autor Halit Alper Tayalıen Limba Engleză Paperback – 30 ian 2025
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Specificații
ISBN-13: 9781032192017
ISBN-10: 1032192011
Pagini: 122
Ilustrații: 16
Dimensiuni: 138 x 216 x 7 mm
Greutate: 0.15 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
ISBN-10: 1032192011
Pagini: 122
Ilustrații: 16
Dimensiuni: 138 x 216 x 7 mm
Greutate: 0.15 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Public țintă
Academic and PostgraduateCuprins
Preface
CHAPTER 1: Introduction
CHAPTER 2: Operations planning
CHAPTER 3: Manufacturing processes
CHAPTER 4: Facility layout and line balancing
CHAPTER 5: Project scheduling
CHAPTER 6: Work center scheduling
CHAPTER 7: Linear programming in operations planning
CHAPTER 8: Aggregate operations planning
REFERENCES
CHAPTER 1: Introduction
CHAPTER 2: Operations planning
CHAPTER 3: Manufacturing processes
CHAPTER 4: Facility layout and line balancing
CHAPTER 5: Project scheduling
CHAPTER 6: Work center scheduling
CHAPTER 7: Linear programming in operations planning
CHAPTER 8: Aggregate operations planning
REFERENCES
Notă biografică
Halit Alper Tayalı is an assistant professor of business with expertise in enhancing productivity and efficiency. As a co-editor of Istanbul Business Research, an ESCI-indexed journal, his commitment to advancing research and knowledge in the field is evident. With a solid academic background, including studies at Istanbul American Robert College, Koç University, and ESC Rennes, he has practical experience through involvement in his family business. His entrepreneurial spirit led him to co-found a software development startup with support from the Scientific and Technological Research Council of Türkiye, highlighting his dedication to innovation and practical applications. As his academic journey continues at Istanbul University, he empowers individuals in the realm of production planning and beyond, driving progress and excellence.
Descriere
This book aims to provide the readers with methodologies, and reviews trends and issues in forecasting in production planning. Furthermore, it presents machine learning methods used in time series forecasting.