This paper presents a new complex optimization problem in the field of automatic design of advanced industrial systems and proposes a hybrid optimization approach to solve the problem. The problem is multi-objective as it aims at finding solutions that minimize CO2 emissions, transportation time, and costs. The optimization approach combines an evolutionary algorithm and classical mathematical programming to design resilient and sustainable global manufacturing networks. Further, it makes use of the OWL ontology for data consistency and constraint management. The experimental validation demonstrates the effectiveness of the approach in both single and double sourcing scenarios. The proposed methodology, in general, can be applied to any industry case with complex manufacturing and supply chain challenges.

翻译：本文提出了先进工业系统自动化设计领域中的一个新型复杂优化问题，并提出了一种混合优化方法来解决该问题。该问题属于多目标优化，旨在寻找能够最小化二氧化碳排放、运输时间和成本的解决方案。该优化方法结合了进化算法与经典数学规划，以设计具有弹性且可持续的全球制造网络。此外，该方法利用OWL本体来确保数据一致性和约束管理。实验验证证明了该方法在单一和双重采购场景下的有效性。总体而言，所提出的方法论可应用于任何面临复杂制造与供应链挑战的行业案例。