Rescheduling Under Disruptions in Manufacturing Systems - Models and Algorithms
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This book views scheduling theory as practical theory, and it has made sure to emphasize the practical aspects of its topic coverage. Thus, this book considers some scenarios existing in most real-world environments, such as preventive machine maintenance, and deteriorating effect where the actual processing time of a job gets longer along with machine’s usage and age. To alleviate the effect of disruption events, some flexible strategies are adopted, including allocation extra resources to reduce job processing times or rejection the production of some jobs. For each considered scenario, depending on the model settings and on the disruption events, this book addresses the complexity, and the design of efficient exact or approximated algorithms. Especially when optimization methods and analytic tools fall short, this book stresses metaheuristics including improved elitist non-dominated sorting genetic algorithm and differential evolution algorithm. This book also provides extensive numerical studies to evaluate the performance of the proposed algorithms. The problem of rescheduling in the presence of unexpected disruption events is of great importance for the successful implementation of real-world scheduling systems. There is now an astounding body of knowledge in this field. This book is the first monograph on rescheduling. It aims at introducing the author's research achievements in rescheduling. It is written for researchers and Ph.D. students working in scheduling theory and other members of scientific community who are interested in recent scheduling models. Our goal is to enable the reader to know about some new achievements on this topic.
Identifies novel rescheduling models in the presence of unexpected disruption events
Provides efficient exact or approximated solution algorithms
Yunqiang Yin received the Ph.D. degree in applied mathematics from Beijing Normal University, Beijing, China, in 2009. He is currently a Professor with the School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China. He has published over 80 papers in journals such as Naval Research Logistics, Omega, European Journal of Operational Research, IEEE Transactions on Systems, Man, and Cybernetics: Systems, International Journal of Production Research, Information Sciences, International Journal of Economics, Annals of Operations Research, Journal of Scheduling, and Computers & Operations Research. He has coauthored 12 books published by Chapman and Hall, McGraw-Hill, and Springer. His current research interests include Operations Research and Optimization, and Logistics Management. He was named one of the “most cited scientists” in computer science by the Elsevier in 2014 to 2018, respectively.
Yaochu Jin (M’98-SM’02-F’16) received the B.Sc., M.Sc., and Ph.D. degrees from Zhejiang University, Hangzhou, China, in 1988, 1991, and 1996 respectively, and the Dr.-Ing. degree from Ruhr University Bochum, Germany, in 2001. He is a Professor in Computational Intelligence, Head of the Nature Inspired Computing and Engineering (NICE) group, Co-Coordinator of the Centre for Mathematical and Computational Biology (CMCB), Department of Computer Science, University of Surrey, Guildford, U.K. He is also a Finland Distinguished Professor (2015-17) with the Industrial Optimization Group, Department of Mathematical Information, University of Jyvaskyla, Finland, and a Changjiang Distinguished Visiting Professor (2015-17), State Key Laboratory of Synthetical Automation of Process Industry, Northeastern University, China. His research interests mainly include evolutionary optimization, machine learning, and their real-world applications. He has (co)authored over 300 peer-reviewed journal and conference papers. His science-driven research interests lie in the interdisciplinary areas that bridge the gap between computational intelligence, computational neuroscience, and computational biology. He is also particularly interested in real-world problem solving using artificial intelligence and machine learning, including data-driven optimization, image identification, and interpretable and secure machine learning. He has (co)authored over 200 peer-reviewed journal and conference papers and been granted eight patents on evolutionary optimization. His research has been funded by EC FP7, UK EPSRC and international companies. He has delivered over 30 invited keynote speeches at international conferences. He is a Professor in Computational Intelligence, Head of the Nature Inspired Computing and Engineering (NICE) group, Co-Coordinator of the Centre for Mathematical and Computational Biology (CMCB), Department of Computer Science, University of Surrey, Guildford, U.K. He is also a Finland Distinguished Professor (2015-17) with the Industrial Optimization Group, Department of Mathematical Information, University of Jyvaskyla.
"The book presents an interesting inside to problems when scheduling is disturbed by breakdowns, unavailable jobs or arriving jobs during processing possibly including maintenance phases. All problems are NP-hard and are solved individually with an assortment of different ideas and all models are tested numerically in practice." (Helmut G. Kahlbacher, zbMATH 1462.90005, 2021)