A Model-Based Intelligent Agent Approach for Supply Chain Transparency and Resilience

基于模型的智能代理方法实现供应链透明度和弹性

• 批准号: 2034974
• 负责人: Kira Barton
• 金额: $ 40.89万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2034974&HistoricalAwards=false
• 关键词: Model; Based; Intelligent; Agent; Approach

This award will contribute to the national prosperity by providing new methods to improve resilience and efficiency of global supply chains, enabling them to respond to unexpected disruptions promptly and proactively. Supply chains are critical components of all commercial enterprises, and as the COVID-19 pandemic has demonstrated, supply chain efficiency must be balanced with the ability of supply chains to respond to major disruptions. This project will investigate models for monitoring and operating supply chains using intelligent agents to represent supply chain components, from raw material suppliers, intermediate producers, assemblers, distributors, to end users. The agents have the ability to communicate, share information and negotiate with other agents, as well as to use this information to make local decisions in real time with other agent's status and objectives in mind. The research is expected to lead towards improved operations with lower costs in supply chain management and increased resilience in the face of large-scale disruptions. The project will build data dashboards and open-source software that will be widely disseminated to support supply-chain risk management. The research activities are integrated with educational and outreach activities, including participation in the Detroit Area Pre-College Engineering Program. This award will develop, validate, and calibrate a cloud-based intelligent agent modeling architecture to characterize information, communication, and negotiation in complex supply chains. Specific research tasks include: characterization of the set of supply chain components that will be modeled with agents, their performance objectives and control activities; construction of communication links and information sets that each agent uses to negotiate with other agents; development of methods for different agents to optimize their actions given their own risk attitudes and uncertainty characterization; and specification of the classes of disturbances in supply chains and development of mechanisms to detect them promptly. The framework is expected to improve modern supply chain management through a new understanding of the characteristics and impact of disruptions and of the requirements and limitations of agent-based technology in supply chain management. The project will deliver a new distributed control framework with modeling flexibility, communication transparency, and the ability to respond to supply chain disruptions. The approach will be validated through a real-world case study in automotive manufacturing.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.

该奖项将通过提供新方法来提高全球供应链的弹性和效率，使它们能够迅速、主动地应对意外中断，从而为国家繁荣做出贡献。 供应链是所有商业企业的关键组成部分，正如 COVID-19 大流行所证明的那样，供应链效率必须与供应链应对重大干扰的能力相平衡。 该项目将研究使用智能代理来代表供应链组件（从原材料供应商、中间生产商、组装商、分销商到最终用户）的供应链监控和运营模型。代理能够与其他代理进行沟通、共享信息和协商，并利用这些信息在考虑其他代理的状态和目标的情况下实时做出本地决策。该研究预计将改善运营，降低供应链管理成本，并提高面对大规模中断的弹性。该项目将构建数据仪表板和开源软件，这些软件将被广泛传播以支持供应链风险管理。 研究活动与教育和外展活动相结合，包括参与底特律地区大学预科工程项目。该奖项将开发、验证和校准基于云的智能代理建模架构，以表征复杂供应链中的信息、通信和谈判。 具体的研究任务包括： 描述将用代理建模的供应链组件集、其绩效目标和控制活动；构建每个代理用于与其他代理协商的通信链路和信息集；根据不同主体自身的风险态度和不确定性特征，制定优化其行动的方法；明确供应链中的干扰类别，并制定及时检测这些干扰的机制。该框架预计将通过对供应链管理中中断的特征和影响以及基于代理的技术的要求和局限性的新理解来改进现代供应链管理。 该项目将提供一个新的分布式控制框架，具有建模灵活性、通信透明度以及响应供应链中断的能力。该方法将通过汽车制造领域的真实案例研究进行验证。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Model-based Multi-agent Framework to Enable an Agile Response to Supply Chain Disruptions*

• DOI: 10.1109/case49997.2022.9926559
• 发表时间: 2022-07
• 期刊: 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)
• 作者: Mingjie Bi;Gongyu Chen;D. Tilbury;Siqian Shen;K. Barton