Overview
- Investigates an assembly line balancing problem under learning effect and uncertain demand
- Utilizes the uncertainty theory to model uncertain task times and considers incompatible task sets constraints
- Performs a case study of COVID-19 and provides some insightful conclusions for the mask manufacturer
Part of the book series: Engineering Applications of Computational Methods (EACM, volume 8)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
About this book
This book introduces several mathematical models in assembly line balancing based on stochastic programming and develops exact and heuristic methods to solve them. An assembly line system is a manufacturing process in which parts are added in sequence from workstation to workstation until the final assembly is produced. In an assembly line balancing problem, tasks belonging to different product models are allocated to workstations according to their processing times and precedence relationships among tasks. It incorporates two features, uncertain task times, and demand volatility, separately and simultaneously, into the conventional assembly line balancing model. A real-life case study related to the mask production during the COVID-19 pandemic is presented to illustrate the application of the proposed framework and methodology. The book is intended for graduate students who are interested in combinatorial optimizations in manufacturing with uncertain input.
Similar content being viewed by others
Keywords
Table of contents (7 chapters)
-
Front Matter
Authors and Affiliations
About the author
Dr. Yuchen Li received his B.E. degrees in Systems Engineering from Beihang University, Beijing, China, in 2010, and the M.Sc. degree in Operations Research from Columbia University, New York, in 2012, and the Ph.D. degree in Industrial Engineering from Rutgers University, New Brunswick, in 2016. Since Nov. 2016, Dr. Li has been with the School of Economics and Management, Beijing University of Technology.
Dr. Yuchen Li is broadly interested in combinatorial optimization in manufacturing with particular emphasis on assembly line balancing area. His research generally involves the design of the intelligent production systems, applied mathematical modeling of manufacturing and industrial systems, and algorithm development.
Bibliographic Information
Book Title: Assembly Line Balancing under Uncertain Task Time and Demand Volatility
Authors: Yuchen Li
Series Title: Engineering Applications of Computational Methods
DOI: https://doi.org/10.1007/978-981-19-4215-0
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022
Hardcover ISBN: 978-981-19-4214-3Published: 11 September 2022
Softcover ISBN: 978-981-19-4217-4Published: 12 September 2023
eBook ISBN: 978-981-19-4215-0Published: 10 September 2022
Series ISSN: 2662-3366
Series E-ISSN: 2662-3374
Edition Number: 1
Number of Pages: XII, 152
Number of Illustrations: 37 b/w illustrations, 7 illustrations in colour
Topics: Industrial and Production Engineering, Mathematical Modeling and Industrial Mathematics, Optimization