<p>Product Description      This book illustrates the main characteristics, challenges and optimisation requirements of robotic disassembly. It provides a comprehensive insight on two crucial optimisation problems in the areas of robotic disassembly through a group of unified mathematical models. The online and offline optimisation of the operational sequence to dismantle a product, for example, is represented with a list of conflicting objectives and constraints. It allows the decision maker and the robots to match the situation automatically and efficiently. To identify a generic solution under different circumstances, classical metaheuristics that can be used for the optimisation of robotic disassembly are introduced in detail. A flexible framework is then presented to implement existing metaheuristics for sequence planning and line balancing in the circumstance of robotic disassembly. Optimisation of Robotic Disassembly for Remanufacturing provides practical case studies on typical product instances to help practitioners design efficient robotic disassembly with minimal manual operation, and offers comparisons of the state-of-the-art metaheuristics on solving the key optimisation problems. Therefore, it will be of interest to engineers, researchers, and postgraduate students in the area of remanufacturing.      From the Back Cover      This book illustrates the main characteristics, challenges and optimisation requirements of robotic disassembly. It provides a comprehensive insight on two crucial optimisation problems in the areas of robotic disassembly through a group of unified mathematical models. The online and offline optimisation of the operational sequence to dismantle a product, for example, is represented with a list of conflicting objectives and constraints. It allows the decision maker and the robots to match the situation automatically and efficiently. To identify a generic solution under different circumstances, classical metaheuristics that can be used for the optimisation of robotic disassembly are introduced in detail. A flexible framework is then presented to implement existing metaheuristics for sequence planning and line balancing in the circumstance of robotic disassembly. Optimisation of Robotic Disassembly for Remanufacturing provides practical case studies on typical product instances to help practitioners design efficient robotic disassembly with minimal manual operation, and offers comparisons of the state-of-the-art metaheuristics on solving the key optimisation problems. Therefore, it will be of interest to engineers, researchers, and postgraduate students in the area of remanufacturing.      About the Author      Assistant Professor Yuanjun Laili is currently a lecturer at School of Automation Science and Electrical Engineering, Beihang University (Beijing University of Aeronautics and Astronautics), China, since September 2015. She obtained her BS, MS, and PhD degrees in 2009, 2012, and 2015 from School of Automation Science and Electrical Engineering in Beihang University, China. From 2017 to 2018 she worked as a research scholar at the Department of Mechanical Engineering, University of Birmingham, UK. She was elected in the ‘Young Talent Lift’ project of China Association for Science and Technology, and was awarded as the ‘Young Simulation Scientist’ of the Society for Modeling & Simulation International (SCS). She is an associate Editor of ‘International Journal of Modeling, Simulation and Scientific Computing’. Her research interests include system modelling and simulation, evolutionary computation, and optimisation in manufacturing systems. She is the author of one monograph and 28 journal and conference articles of these subjects.Yongjing Wang is a scientist and a research engineer at School of Engineering, University of Birmingham, UK, where he has been a key member of Autonomous Remanufacturing Laboratory since its foundation in 2016. He is the designer and manager of the lab and a leadin</p>

Optimisation of Robotic Disassembly for Remanufacturing (Springer Series in Advanced Manufacturing)

Computers & Technology

Laili, Yuanjun

Wang, Yongjing

Fang, Yilin

Pham, Duc Truong

Computer Science

Springer

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