混合GA/MSDLO方法解决绿地机器人化机床上下料布局问题
Hybrid GA/MSDLO Approach to Solve a Greenfield Robotized Machine Tending Layout
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中文总结 AI 辅助
针对绿地机器人化机床上下料布局问题，提出先遗传算法寻优初始布置、再MSDLO局部微调的混合方法，兼顾全局探索与局部优化，满足工业需求。
中文摘要 AI 辅助
遗传算法（GA）等元启发式算法能有效探索解空间，但往往产生次优结果，且对参数调整敏感。相比之下，质量-弹簧-阻尼布局优化（MSDLO）方法速度快，擅长局部优化资源布置，但其有效性取决于初始资源放置。本文首先采用遗传算法在绿地布局中建立最优资源放置，然后应用MSDLO微调这些资源的位置和方向。文中展示并讨论了结果，强调了该方法在有效满足工业需求方面的实用性。
英文摘要
Meta-heuristics like genetic algorithms (GAs) effectively explore solution spaces but often yield suboptimal results and are sensitive to parameter tuning. In contrast, the mass-spring-damper layout optimization (MSDLO) method is fast and excels in locally optimizing resource arrangements, though its effectiveness depends on the initial resource placement. This paper first employs genetic algorithms to establish an optimal resource placement in a greenfield layout. We then apply MSDLO to fine-tune the positions and orientations of these resources. The results are presented and discussed, highlighting this approach's practicality in effectively addressing industry needs.
发表机构
- Fraunhofer IPA（弗劳恩霍夫工业生产研究所）
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