Smart Supply Chain Management via Data Driven Optimization

通过数据驱动优化实现智能供应链管理

• 批准号: RGPIN-2019-07115
• 负责人: Zhang, Guoqing
• 金额: $ 3.13万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760503
• 关键词: Smart; Supply; Chain; Management; via

With the advances of innovation technologies, such as internet of things, big data, cloud computing, AI, and blockchain, the real-time visibility and integration of supply chains are evolving to become more and more cost-effective, information-shared and environmental-aware. On another aspect, the different partners of supply chains face new challenges arising from the new manufacturing/business models, highly dynamic environments, and different risks from uncertainty to new techniques' application. Thus smart supply chains are to be studied that have the ability to connect the different partners, reconfigure and optimize the existing supply networks, and even have the intelligent capacity to plan, control and adjust the system to match the supply to demands in a real-time and effective way. In this proposed research, we will study the new challenges faced by supply chains under digital environments and new business models, develop a novel framework for smart supply chains via data driven hybrid optimization methods and other approaches. In details, we will work on the following: 1). Study the impacts and opportunities of new digital technologies (such as internet of things (IoT) and cloud service), and new business models (such as e-commerce and omnichannel) on modern supply chains. 2). Develop new concepts and methods for smart supply chain management: we will focus on some critical problems in the strategic, tactical and operational phases of supply chains and logistics management under a digital manufacturing/business environment, including strategic design of smarter supply networks under the IoT and uncertainty, tactical integrated decisions for production planning, procurement, distribution, and sales with digital technology, smart warehouse operation and management under IoT and omnichannel supply chains. 3). Develop data driven optimization models, methods and hybrid algorithms, i.e., studying how to combine data analysis/data driven methods with classical optimization methods to solve complex supply chain management problems. The integration of optimization method with machine learning will also be studied. 4). Work with our partners in automotive, medical, and food industries, solve the real-world supply chain problems under digital manufacturing/business environment, verify the proposed frame and new solution approaches, and make the effort for the transfer of the results from this research to the applicable industries.

随着物联网、大数据、云计算、人工智能和区块链等创新技术的进步，供应链的实时可视性和集成正在发展，变得越来越具有成本效益、信息共享和环境意识。另一方面，供应链的不同合作伙伴面临着新的制造/商业模式、高度动态的环境以及新技术应用的不确定性带来的不同风险所带来的新挑战。因此，智能供应链将被研究，有能力连接不同的合作伙伴，重新配置和优化现有的供应网络，甚至有智能的能力来规划，控制和调整系统，以实时和有效的方式将供应与需求相匹配。在这项拟议的研究中，我们将研究数字环境和新商业模式下供应链面临的新挑战，通过数据驱动的混合优化方法和其他方法开发智能供应链的新框架。具体而言，我们将做以下工作：1）。研究新的数字技术（如物联网（IoT）和云服务）和新的商业模式（如电子商务和全渠道）对现代供应链的影响和机遇。2）的情况。开发智能供应链管理的新概念和方法：我们将专注于数字化制造/商业环境下供应链和物流管理的战略，战术和运营阶段的一些关键问题，包括物联网和不确定性下智能供应网络的战略设计，数字技术下生产计划，采购，分销和销售的战术集成决策，物联网和全渠道供应链下的智能仓库运营和管理。（3）第三章。开发数据驱动的优化模型、方法和混合算法，即，研究如何将联合收割机数据分析/数据驱动方法与经典优化方法相结合来解决复杂的供应链管理问题。最优化方法与机器学习的集成也将进行研究。4）.与汽车、医疗和食品行业的合作伙伴合作，解决数字化制造/商业环境下的现实供应链问题，验证所提出的框架和新的解决方案，并努力将研究成果转移到适用的行业。