CAREER: A Multi-layer Dynamic Network Control for Agile, Optimized, and Sustainable Supply Chains

事业：敏捷、优化和可持续供应链的多层动态网络控制

• 批准号: 2238269
• 负责人: Shuo Wang
• 金额: $ 50.3万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2028-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238269&HistoricalAwards=false
• 关键词: CAREER; Multi; layer; Dynamic; Network

This Faculty Early Career Development (CAREER) project will support an integrated research, education, and outreach program in supply chain network (SCN) management. The main aim is to broaden the focus of SCN management from economic efficiency to accuracy, agility, and sustainability through dynamic network modeling and data-driven decision-making methods. The current supply chain analysis tool, relying on static optimization, is insensitive to non-eligible changes such as policy changes that may cause economic turmoil. By bridging supply chain management with the optimal network control concepts, this work will interpret the processes of producing and distributing goods of an established SCN in the dynamic form to capture the non-negligible changes more rapidly and maximize the utility. In addition to ushering in the concept of dynamic modeling for agile reaction to changes and effective decision-making on network management, this work will further discuss supply chains involving recycling processes through a multi-layer network model. The broad applicability of this work will provide expert guidance to local industries for improving SCN efficiency, profit, or recycling and will further promote awareness of the current waste issues and facilitate the local community's engagement toward green manufacturing and sustainability. In addition, in the long run, this project will deliver reliable solutions for many other SCNs that are crucial for the economic developments of many companies in related fields. This study will also adopt dynamic planning concepts for decision-making related to STEM student recruitment, retention, and training, which will facilitate engineering education reform from experience-based management to data-driven strategic planning. The research will address problems related to the current poor management of SCNs that heavily rely on the power of computational devices by modeling supply chains as dynamic systems that capture the non-eligible changes and transforming SCN management from static optimization to model-based dynamic control. This model enables quantitative analysis of optimal strategies such as daily production and optimal distribution route, where a parallel computing scheme will be developed for computational efficiency. A demonstration metric on the feasibility and applicability of the proposed work, based on dynamic modeling, will be introduced for evaluation, which sets the foundation for the exploration of other complex dynamic networks. Furthermore, a multi-layer network will be adopted to improve the efficiency of SCNs, which will result in discussions on applications involving recycling processes for sustainability management.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个教师早期职业发展（Career）项目将支持供应链网络（SCN）管理方面的综合研究、教育和推广计划。主要目标是通过动态网络建模和数据驱动的决策方法，将SCN管理的重点从经济效率扩展到准确性、敏捷性和可持续性。目前的供应链分析工具依赖于静态优化，对可能导致经济动荡的政策变化等不符合条件的变化不敏感。通过将供应链管理与最优网络控制概念相结合，本工作将以动态形式解释已建立的供应链网络的生产和分配过程，以更快地捕捉不可忽略的变化并最大化效用。除了引入动态建模的概念，以实现对变化的敏捷反应和对网络管理的有效决策，这项工作还将通过多层网络模型进一步讨论涉及回收过程的供应链。这项工作的广泛适用性将为当地工业提供专家指导，以提高SCN的效率、利润或回收利用，并将进一步提高对当前废物问题的认识，并促进当地社区参与绿色制造和可持续发展。此外，从长远来看，该项目将为许多其他scn提供可靠的解决方案，这些解决方案对相关领域许多公司的经济发展至关重要。本研究还将采用动态规划概念来制定与STEM学生招聘、保留和培训相关的决策，这将促进工程教育从基于经验的管理向数据驱动的战略规划的改革。该研究将通过将供应链建模为捕获不符合条件的变化的动态系统，并将SCN管理从静态优化转变为基于模型的动态控制，解决与当前严重依赖计算设备的SCN管理不善相关的问题。该模型可以定量分析最优策略，如每日生产和最优分配路线，其中并行计算方案将开发计算效率。本文将引入基于动态建模的可行性和适用性论证指标进行评估，为探索其他复杂动态网络奠定基础。此外，我们亦会采用多层网络，以提高可持续发展管理系统的效率，并讨论回收过程的应用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。