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Simultaneous optimization of layout and task schedule for robotic cellular manufacturing systems

机译:同时优化机器人蜂窝制造系统的布局和任务计划

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Multi-robot cellular manufacturing systems utilize several articulated industrial robots that cooperatively perform a large number of complex operations when assembling products. To enhance the system performance when designing layouts for such robotic cellular manufacturing systems, the positions of robots and other manufacturing system components must be appropriately determined by considering the sequence of tasks the robots conduct during the assembly process. This paper proposes a new mul-tiobjective layout design optimization technique for robotic cellular manufacturing system layouts that can simultaneously determine the positions of manufacturing components and also task scheduling. First, in this paper, sequence-pair representation is used to specify layout design candidates that inherently avoid interference between assembly system components, and the use of dummy components is introduced to represent layout areas that provide necessary and appropriate spacing between manufacturing system components. In addition, task allocations for each robot are considered as discrete design variables. The design criteria for robot cellular manufacturing system layout designs are clarified and the layout design problem is formulated as a multiobjective optimization problem. To solve the optimization problem, a method based on a multiobjective genetic algorithm is proposed and numerical examples are provided to demonstrate the effectiveness of the proposed method.
机译:多机器人蜂窝制造系统利用了多个铰接的工业机器人,它们在组装产品时协同执行大量复杂的操作。为了在设计此类机器人蜂窝式制造系统的布局时提高系统性能,必须通过考虑机器人在组装过程中执行的任务顺序来适当确定机器人和其他制造系统组件的位置。本文提出了一种新的多目标机器人蜂窝制造系统布局优化设计技术,该技术可以同时确定制造组件的位置以及任务调度。首先,在本文中,序列对表示用于指定固有避免装配系统组件之间干扰的布局设计候选,并且引入了虚拟组件来表示在制造系统组件之间提供必要和适当间距的布局区域。此外,每个机器人的任务分配被视为离散的设计变量。阐明了机器人蜂窝制造系统布局设计的设计标准,并将布局设计问题表述为多目标优化问题。为了解决优化问题,提出了一种基于多目标遗传算法的方法,并通过数值算例验证了该方法的有效性。

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