提出混合优化方法，设计低碳高效可持续的全球制造网络。
Towards Resilient and Sustainable Global Industrial Systems: An Evolutionary-Based Approach
- 融合进化算法与数学规划，优化多目标制造系统。
- 在单双源场景下均降低碳排放、运输时间与成本。
- 适合有复杂供应链挑战的制造业企业参考。
本文提出一个新型复杂优化问题，用于自动设计先进工业系统，并提出一种混合优化方法求解。该问题为多目标优化，旨在最小化二氧化碳排放、运输时间和成本。所提方法结合进化算法与经典数学规划，以设计具有韧性和可持续性的全球制造网络。同时，利用OWL本体实现数据一致性和约束管理。实验验证表明，该方法在单源和双源场景下均具有效性。总体而言，该方法可适用于任何面临复杂制造与供应链挑战的行业案例。
原文摘要 · Abstract (English)
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.
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