The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Boris Shnits - One of the best experts on this subject based on the ideXlab platform.
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a genetic algorithm for Robotic Assembly line balancing
European Journal of Operational Research, 2006Co-Authors: Gregory Levitin, Jacob Rubinovitz, Boris ShnitsAbstract:Flexibility and automation in Assembly lines can be achieved by the use of robots. The Robotic Assembly line balancing (RALB) problem is defined for Robotic Assembly line, where different robots may be assigned to the Assembly tasks, and each robot needs different Assembly times to perform a given task, because of its capabilities and specialization. The solution to the RALB problem includes an attempt for optimal assignment of robots to line stations and a balanced distribution of work between different stations. It aims at maximizing the production rate of the line. A genetic algorithm (GA) is used to find a solution to this problem. Two different procedures for adapting the GA to the RALB problem, by assigning robots with different capabilities to workstations are introduced: a recursive assignment procedure and a consecutive assignment procedure. The results of the GA are improved by a local optimization (hill climbing) work-piece exchange procedure. Tests conducted on a set of randomly generated problems, show that the Consecutive Assignment procedure achieves, in general, better solution quality (measured by average cycle time). Further tests are conducted to determine the best combination of parameters for the GA procedure. Comparison of the GA algorithm results with a truncated Branch and Bound algorithm for the RALB problem, demonstrates that the GA gives consistently better results.
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a genetic algorithm for Robotic Assembly line balancing
IFAC Proceedings Volumes, 2002Co-Authors: Gregory Levitin, Jacob Rubinovitz, Boris ShnitsAbstract:Abstract Robotic Assembly line balancing (RALB) is specific for Robotic Assembly, where different robots require different Assembly times to perform the same task because of their specialization. The problem includes optimal assignment of robots to line stations and balanced distribution of work between different stations. It aims at maximizing the production rate of the line. This problem is solved by a genetic algorithm (GA). Different procedures for adapting the GA to the RALB problem are introduced, including a local optimization (hill climbing) work-piece exchange procedure. The best combination of these procedures is reached by testing on a set of randomly generated problems.
George A. Bekey - One of the best experts on this subject based on the ideXlab platform.
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ICRA - Integrating Robotic Assembly planning and scheduling: a two-dimensional view
Proceedings. IEEE International Conference on Robotics and Automation, 1Co-Authors: X.d. Xia, George A. BekeyAbstract:A unified framework is proposed for constructing embedded Robotic Assembly systems that integrate plan generation and plan execution (scheduling). The salient feature of this framework is that Robotic Assembly planning and scheduling are organized into two orthogonal problem-solving processes that can be carried out in two distinctive modes: an interleaved or a temporarily separated manner. The utility and advantages of this two-dimensional framework are demonstrated in the context of a fully integrated planning and scheduling system which has been implemented and tested in the domain of Robotic Assembly. >
Jacob Rubinovitz - One of the best experts on this subject based on the ideXlab platform.
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a genetic algorithm for Robotic Assembly line balancing
European Journal of Operational Research, 2006Co-Authors: Gregory Levitin, Jacob Rubinovitz, Boris ShnitsAbstract:Flexibility and automation in Assembly lines can be achieved by the use of robots. The Robotic Assembly line balancing (RALB) problem is defined for Robotic Assembly line, where different robots may be assigned to the Assembly tasks, and each robot needs different Assembly times to perform a given task, because of its capabilities and specialization. The solution to the RALB problem includes an attempt for optimal assignment of robots to line stations and a balanced distribution of work between different stations. It aims at maximizing the production rate of the line. A genetic algorithm (GA) is used to find a solution to this problem. Two different procedures for adapting the GA to the RALB problem, by assigning robots with different capabilities to workstations are introduced: a recursive assignment procedure and a consecutive assignment procedure. The results of the GA are improved by a local optimization (hill climbing) work-piece exchange procedure. Tests conducted on a set of randomly generated problems, show that the Consecutive Assignment procedure achieves, in general, better solution quality (measured by average cycle time). Further tests are conducted to determine the best combination of parameters for the GA procedure. Comparison of the GA algorithm results with a truncated Branch and Bound algorithm for the RALB problem, demonstrates that the GA gives consistently better results.
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a genetic algorithm for Robotic Assembly line balancing
IFAC Proceedings Volumes, 2002Co-Authors: Gregory Levitin, Jacob Rubinovitz, Boris ShnitsAbstract:Abstract Robotic Assembly line balancing (RALB) is specific for Robotic Assembly, where different robots require different Assembly times to perform the same task because of their specialization. The problem includes optimal assignment of robots to line stations and balanced distribution of work between different stations. It aims at maximizing the production rate of the line. This problem is solved by a genetic algorithm (GA). Different procedures for adapting the GA to the RALB problem are introduced, including a local optimization (hill climbing) work-piece exchange procedure. The best combination of these procedures is reached by testing on a set of randomly generated problems.
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RALB – A Heuristic Algorithm for Design and Balancing of Robotic Assembly Lines
CIRP Annals, 1993Co-Authors: Jacob Rubinovitz, Joseph Bukchin, E. LenzAbstract:Summary This paper describes an heuristic approach for design and balancing of a Robotic Assembly line. The objective of RALB (Robotic Assembly Line Balancing) algorithm is to balance the Assembly line, by allocating equal amount of work to the stations on the line, while assigning the most efficient robot type, out of several different types of robots available for the Assembly task, to each workstation, and minimizing the number of workstations and robots used. RALB uses heuristics to limit and guide a Branch and Bound frontier starch, thus leading to solution of very large or difficult problems. A recommendation of the optimal set of heuristic rules is made based on results of extensive testing of RALB with a variety of Assembly problems.
Gregory Levitin - One of the best experts on this subject based on the ideXlab platform.
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a genetic algorithm for Robotic Assembly line balancing
European Journal of Operational Research, 2006Co-Authors: Gregory Levitin, Jacob Rubinovitz, Boris ShnitsAbstract:Flexibility and automation in Assembly lines can be achieved by the use of robots. The Robotic Assembly line balancing (RALB) problem is defined for Robotic Assembly line, where different robots may be assigned to the Assembly tasks, and each robot needs different Assembly times to perform a given task, because of its capabilities and specialization. The solution to the RALB problem includes an attempt for optimal assignment of robots to line stations and a balanced distribution of work between different stations. It aims at maximizing the production rate of the line. A genetic algorithm (GA) is used to find a solution to this problem. Two different procedures for adapting the GA to the RALB problem, by assigning robots with different capabilities to workstations are introduced: a recursive assignment procedure and a consecutive assignment procedure. The results of the GA are improved by a local optimization (hill climbing) work-piece exchange procedure. Tests conducted on a set of randomly generated problems, show that the Consecutive Assignment procedure achieves, in general, better solution quality (measured by average cycle time). Further tests are conducted to determine the best combination of parameters for the GA procedure. Comparison of the GA algorithm results with a truncated Branch and Bound algorithm for the RALB problem, demonstrates that the GA gives consistently better results.
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a genetic algorithm for Robotic Assembly line balancing
IFAC Proceedings Volumes, 2002Co-Authors: Gregory Levitin, Jacob Rubinovitz, Boris ShnitsAbstract:Abstract Robotic Assembly line balancing (RALB) is specific for Robotic Assembly, where different robots require different Assembly times to perform the same task because of their specialization. The problem includes optimal assignment of robots to line stations and balanced distribution of work between different stations. It aims at maximizing the production rate of the line. This problem is solved by a genetic algorithm (GA). Different procedures for adapting the GA to the RALB problem are introduced, including a local optimization (hill climbing) work-piece exchange procedure. The best combination of these procedures is reached by testing on a set of randomly generated problems.
Thomas Spengler - One of the best experts on this subject based on the ideXlab platform.
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Redundant configuration of Robotic Assembly lines with stochastic failures
International Journal of Production Research, 2017Co-Authors: Christoph Müller, Martin Grunewald, Thomas SpenglerAbstract:One of the main challenges in the operation of Robotic Assembly lines is the occurrence of failures. Due to the connection of the stations via a material handling system, failures at one station of...
