Optimizing evolving manufacturing systems often requires considering the interplay of various architectural design decisions (ADDs), especially at the lower levels of the automation pyramid. Here, decisions about programmable logic controllers, stations, and shop-floor topology directly impact product characteristics. This necessitates a multidimensional design space exploration (DSE) approach that synthesizes diverse ADDs together. However, integrating diverse ADDs within the complex context of industrial automation remains a challenge, and existing DSE solutions from other domains often lack direct applicability. This work introduces a novel multidimensional DSE approach for such systems, leveraging domain-specific input models to automatically generate valid and optimal deployment, shop-floor topology, and production planning ADDs. We evaluate the approach by assessing the quality of derived ADDs against various domain optimization goals. This evaluation compares our approach to both random and manual ADDs, utilizing a prototype implementation and realistic manufacturing architectures.

中文翻译：

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不断发展的制造系统的多维设计空间探索：部署、拓扑和生产规划架构决策的相互作用


优化不断发展的制造系统通常需要考虑各种架构设计决策 (ADD) 的相互作用，特别是在自动化金字塔的较低级别。在这里，有关可编程逻辑控制器、站和车间拓扑的决策直接影响产品特性。这就需要一种将不同 ADD 综合在一起的多维设计空间探索 (DSE) 方法。然而，在复杂的工业自动化环境中集成不同的 ADD 仍然是一个挑战，并且来自其他领域的现有 DSE 解决方案通常缺乏直接适用性。这项工作为此类系统引入了一种新颖的多维 DSE 方法，利用特定领域的输入模型自动生成有效且最佳的部署、车间拓扑和生产计划 ADD。我们通过根据各种域优化目标评估派生 ADD 的质量来评估该方法。该评估利用原型实现和实际制造架构，将我们的方法与随机和手动 ADD 进行比较。