Rescheduling Under Disruptions in Manufacturing Systems: Models and Algorithms
เจฎเจพเจฐเจ 2020 ยท Springer Nature
เจ-เจเจฟเจคเจพเจฌ
147
เจชเฉฐเจจเฉ
reportเจฐเฉเจเจฟเฉฐเจเจพเจ เจ
เจคเฉ เจธเจฎเฉเจเจฟเจเจตเจพเจ เจฆเฉ เจชเฉเจธเจผเจเฉ เจจเจนเฉเจ เจเฉเจคเฉ เจเจ เจนเฉ ย เจนเฉเจฐ เจเจพเจฃเฉ
เจเจธ เจ-เจเจฟเจคเจพเจฌ เจฌเจพเจฐเฉ
This book provides an introduction to the models, methods, and results of some rescheduling problems in the presence of unexpected disruption events, including job unavailability, arrival of new jobs, and machine breakdown. The occurrence of these unexpected disruptions may cause a change in the planned schedule, which may render the originally feasible schedule infeasible. Rescheduling, which involves adjusting the original schedule to account for a disruption, is necessary in order to minimize the effects of the disruption on the performance of the system. This involves a trade-off between finding a cost-effective new schedule and avoiding excessive changes to the original schedule.
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 machineโs 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.
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 machineโs 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.
เจฒเฉเจเจ เจฌเจพเจฐเฉ
Dujuan Wang received the B.S. and M.S. degrees in computer science and technology, and the Ph.D. degree in management science and engineering from the Dalian University of Technology (DLUT), Dalian, China. She is currently a professor with the Business School, Sichuan University, Chengdu, China. She has published over 30 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. Her current research interests include scheduling theory, production and operations management, intelligent optimization, and computer applications. She is an Organizer of the IEEE Symposium on Computational Intelligence in E-Government 2016, and has been a member of the Program Committee of the 2015International Conference on Advanced Computational Intelligence 2015 and the Sixth International Conference on Swarm Intelligence 2015.
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 โmost cited scientistsโ in computer science by the Elsevier in 2014 to 2018, respectively.
Yaochu Jin (Mโ98-SMโ02-Fโ16) 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.
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 โmost cited scientistsโ in computer science by the Elsevier in 2014 to 2018, respectively.
Yaochu Jin (Mโ98-SMโ02-Fโ16) 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.
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