# Multidisciplinary Reliability Design Optimization Modeling Based on SysML
## 6 Conclusions  
This paper presents a novel approach to multidisciplinary reliability design optimization (MDO) based on SysML, with a focus on extending SysML to handle optimization problems more effectively. The key contributions and findings of this study can be summarized as follows: (1) Extension of SysML for Optimization. The proposed extensions to SysML, including metamodels for optimization variables, constraints, and objectives, significantly enhance the language's capability to represent complex optimization problems. These extensions provide a comprehensive framework for defining and analyzing various optimization scenarios, improving the integration of system design and optimization processes. (2) Formal Expression and Extraction Methods. The formal expression of MDO problems using extended SysML offers a structured and systematic approach to capturing the internal structure of optimization problems. The use of SysML parametric diagrams and XML-based extraction methods demonstrates a practical approach to managing and processing optimization data, facilitating better model organization and data integration. (3) Practical Application and Validation. The application of the SysML-based optimization model to the in-vehicle environmental control integration system validates the effectiveness of the proposed method. The case study confirms that the extended SysML framework can successfully manage system-level and subsystem-specific optimization objectives, illustrating its potential for practical use in complex engineering systems. Despite the significant advantages of the proposed approach, there are limitations to consider. The complexity of extending SysML and the potential need for specialized tools to support extended metamodels may pose implementation challenges. Additionally, the effectiveness of the method in different application contexts and its performance with respect to larger-scale problems need further investigation. Future work should focus on refining SysML extensions, developing automated tools for model generation, and validating the approach with a wider range of case studies to ensure its reliability and effectiveness in broader applications. While the proposed approach offers significant benefits, several areas for further research and development are identified to enhance its effectiveness and applicability: (1) Refinement of SysML Extensions. Enhancing the flexibility and applicability of SysML extensions to accommodate a broader spectrum of optimization problems is crucial. This includes addressing dynamic constraints, non-linear relationships, and time-varying objectives. Investigating ways to make these extensions more adaptable to different engineering contexts will improve their utility and effectiveness. (2) Development of Automated Tools. There is a need for developing automated tools that can streamline the process of model generation, optimization setup, and real-time integration. These tools should facilitate easier creation and management of SysML-based optimization models, reducing manual effort and improving efficiency. Automation in model generation and solver integration can significantly enhance usability and reduce errors. (3) Expansion of Validation Efforts. To ensure the robustness and generalizability of the approach, further validation is necessary. This includes applying the SysML-based optimization model to a wider range of case studies, including larger-scale and more complex engineering problems. Such validation efforts will provide insights into the approach's performance across different domains and its scalability. (4) Integration with Other Tools and Standards. Exploring the integration of SysML-based optimization models with other tools and data exchange standards is essential for improving interoperability. Investigating how the SysML framework can interact with various software tools, such as optimization solvers and simulation platforms, will enhance its practical applicability and streamline workflows. (5) Addressing Computational Challenges.