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.

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

Autorentext

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.

Klappentext

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.

Inhalt

Preface.- Chapter 1 Introduction.- Chapter 2 State of the art.- Chapter 3 Rebalancing an assembly line with disruptions.- Chapter 4 Two-sided assembly line balancing under uncertain task time attributes.- Chapter 5 System reliability optimization under uncertain task time attributes.- Chapter 6 Assembly line balancing under task learning and uncertain demand.- Chapter 7 A joint assembly line balancing and lot-sizing problem under uncertain demand.- References.