首页|Study Findings on Robotics Are Outlined in Reports from Wuhan University of Tech nology (A Multi-population Cooperative Coevolution Artificial Bee Colony Algorit hm for Partial Multi-robotic Disassembly Line Balancing Problem Considering ...)
Study Findings on Robotics Are Outlined in Reports from Wuhan University of Tech nology (A Multi-population Cooperative Coevolution Artificial Bee Colony Algorit hm for Partial Multi-robotic Disassembly Line Balancing Problem Considering ...)
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024 OCT 08 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Research findings on Robotics are discussed in a new report. According to news reporting originating in Wuhan, People's Republic of China, by NewsRx journalists, research stated, "Existing literature on the ro botic disassembly line balancing problem often assume that robots are always in good working condition. But in fact, due to the inevitable aging of the machines , robots may break down unexpectedly." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), China Scholarship Council. The news reporters obtained a quote from the research from the Wuhan University of Technology, "And the original task assignment would become infeasible, which may lead to unplanned shutdowns of the disassembly line. To tackle this situatio n, a partial multi-robotic disassembly line balancing problem considering preven tive maintenance scenarios (PMRDLBP-PM) is proposed. The objectives of the PMRDL BP-PM not only encompass the traditional goals of robotic disassembly line balan cing problem, such as cycle time and disassembly profitability, but also take in to account the potential additional time and cost incurred from the reconfigurat ion of workstations necessitated by changes in task allocation. Then, a multipo pulation cooperative coevolution artificial bee colony (MPCCABC) algorithm is de veloped. Specifically, to enhance the quality of the initial population and ensu re population diversity, the population is divided into four subpopulations, inc luding three high-quality subpopulations generated by heuristic rules based on o ptimization objectives, and one randomly generated subpopulation. And an adaptiv e progressive neighborhood search strategy is proposed to improve search efficie ncy by adjusting the complexity of neighborhood operations based on search feedb ack. Moreover, a cooperative co-evolution strategy with historical information i s adopted to supplement historical optimal information in subpopulation informat ion exchange, increasing computing resource utilization and accelerating converg ence speed. Finally, three instances are conducted to test the validity of the p roposed model and algorithm."
WuhanPeople's Republic of ChinaAsiaAlgorithmsEmerging TechnologiesMachine LearningNano-robotRoboticsRobo tsWuhan University of Technology
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