| Summary: | 博士 === 國立交通大學 === 工業工程與管理學系 === 86 === The industrial robot has been applied widely in manufacturing. The robot assembly time is heavily related to the production cost and capacity. Therefore, how to reduce the robot assembly time is a crucial issue. Two types of robot assembly motion have been characterized: (1) fixed robot motion between fixed pick and place (FPP) points and (2) robot motion with dynamic pick and place (DPP) points. Three factors highly influence the robot assembly efficiency: (1) robot motion control, (2) the sequence of placement point, and (3) the magazine assignment.
In the robotics assembly problem, the coordinates of assembly point and magazine are reacted dynamically so that the evaluation function is extremely complicated. To route robotics travel, most investigations have utilized the fixed coordinate of insertion points and magazine of the Traveling Salesman Problems (TSP) method to sequence the insertion points after arbitrarily assigning the magazine. However, robotics travel routing should be based on a relative coordinate so as to obtain a better solution because the robotics, board and magazine are simultaneously moved at different speeds during assembly.
To resolve such a dynamically combinatorial problem, this dissertation presents the Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS) based procedures. These approaches can simultaneously arrange the insertion sequence and assign the magazine slots by the computer and yield a better performance than in the conventional approach. Results presented herein also demonstrate that the larger the number of insertion points and/or part numbers implies a better performance. These approaches are also compared.
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