Supply Chain Decision Making Framework Considering Uncertainty

考虑不确定性的供应链决策框架

• 批准号: 2217472
• 负责人: Marianthi Ierapetritou
• 金额: $ 41.75万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2217472&HistoricalAwards=false
• 关键词: Supply; Chain; Decision; Making; Framework

COVID-19 has exacerbated an already fragile supply chain network for many of today’s everyday goods, food supplies, specialty chemicals, fuels, electronics components, and pharmaceuticals. The pandemic tested the flexibility and resilience of global supply chains as major international corporations experienced personnel shortages and other unexpected disruptions to their operations. These challenges motivate a vision for a restructured supply chain of the future, characterized by flexibility and fast adaptability to abrupt changes. To accomplish this, the decision-making time horizon, enterprise complexity, and objectives of the enterprise must be considered when developing a strategy for controlling individual manufacturing units, scheduling tasks in a manufacturing plan to meet product demand, and planning at the enterprise management level to ensure that raw materials are available throughout the supply chain. This research program will develop methodologies and frameworks that target the modernization of the enterprise structure to account for uncertainty and more closely integrate different levels of the supply chain for purified gas and pharmaceutical manufacturing and plastics recycling operations. This will allow for more efficient use of resources, avoid unnecessary waste, and compensate for expected and unexpected events to avoid the breakdown of the supply chain. The research will take place at the University of Delaware and will provide the funds to educate graduate and undergraduate students in this interdisciplinary domain. The PI has a long history of promoting women and underrepresenting minorities in their work. Results from this research will be translated into software tools useful to industry for making better supply chain decisions. Previous attempts to integrate process models, scheduling methods, planning problems and supply chain optimization focused on small scale benchmark problems and were based either on using intuition to incorporate full-scale representations of the lower-level problems into higher levels or used mathematical simplifications of the lower levels to facilitate integration. In this research program, a new approach to integrating decision-making processes is proposed that leverages the large amount of information that typically is available in enterprises and generates decision-making strategies that account for uncertainty in a computationally tractable manner. The PI has extensive expertise in the integration of planning, scheduling, and control problems, as well as in the areas of feasibility analysis and uncertainty quantification. It is proposed that integration and optimization can be achieved by first defining mathematical models for the optimization of each decision-making stage, and then identifying constraints that are dependent on lower-level problems. Among other factors, the feasibility of the lower-level problems is identified as essential information that must be incorporated in the higher-level decision. These constraints are usually complex in form and cannot be accounted for without increasing the dimensionality of the optimization problem and generating intractable formulations. Therefore, it will be demonstrated that data-driven models can be used to obtain simpler forms for the interdependent constraints. These models will be created with the data available from the enterprise, leveraging the potential of big-data analytics and the internet of things. The effects of uncertainty are key to the performance of optimal decisions in the supply chain. In this research program, strategies to minimize the threats posed by operational and disruption uncertainties will be developed. Computational complexity of the final optimization problem will be controlled through the use of decomposition approaches and a rolling horizon strategy.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.

COVID-19 加剧了当今许多日常用品、食品供应、特种化学品、燃料、电子元件和药品本已脆弱的供应链网络，由于大型国际公司经历了人员短缺，这场大流行考验了全球供应链的灵活性和弹性。这些挑战激发了对未来供应链进行重组的愿景，其特点是灵活性和快速适应突然变化的能力，需要决策时间范围、企业复杂性和目标。企业必须考虑的时候制定控制各个制造单位的策略，在制造计划中安排任务以满足产品需求，并在企业管理层进行规划以确保整个供应链中的原材料可用。该研究计划将开发针对以下目标的方法和框架。企业结构的现代化，以解决不确定性，并更紧密地整合纯化气体和药品制造以及塑料回收业务的不同级别的供应链，这将有助于更有效地利用资源，避免不必要的浪费，并补偿预期和意外的损失。该研究将采取措施避免供应链崩溃。该项目在特拉华大学拥有一席之地，并将提供资金来教育这一跨学科领域的研究生和本科生。这项研究的结果将转化为有用的软件工具。行业做出更好的供应链决策的先前尝试将流程模型、调度方法、规划问题和供应链优化集中在小规模基准问题上，并且基于使用直觉将较低级别问题的全面表示纳入其中。更高层次或使用数学简化在该研究项目中，提出了一种集成决策过程的新方法，该方法利用企业中通常可用的大量信息，并生成以计算可处理的方式解释不确定性的决策策略。 PI 在规划、调度和控制问题的集成以及可行性分析和不确定性量化领域拥有广泛的专业知识，建议首先定义每个问题的优化数学模型来实现集成和优化。决策阶段，然后确定约束条件除其他因素外，较低级别问题的可行性被认为是必须纳入较高级别决策的基本信息。这些约束的形式通常很复杂，如果不考虑就无法解释。增加优化问题的维度并生成棘手的公式，将证明数据驱动模型可以用于相互依赖的约束的更简单的形式，这些模型将利用企业提供的数据来创建。大数据分析和互联网不确定性的影响是供应链中最优决策的关键，在该研究项目中，将通过控制最终优化问题的计算复杂性来制定最小化操作和中断不确定性所造成的威胁的策略。使用分解方法和滚动策略。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点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 mathematical modeling approach for supply chain management under disruption and operational uncertainty

中断和运营不确定性下供应链管理的数学建模方法

• DOI: 10.1002/aic.18037
• 发表时间: 2023-01
• 期刊: AIChE Journal
• 影响因子: 3.7
• 作者: Badejo, Oluwadare;Ierapetritou, Marianthi
• 通讯作者: Ierapetritou, Marianthi

Mathematical Programming Approach to Optimize Tactical and Operational Supply Chain Decisions under Disruptions

用于优化中断情况下的战术和运营供应链决策的数学规划方法

• DOI: 10.1021/acs.iecr.2c01641
• 发表时间: 2022-11
• 期刊: Industrial & Engineering Chemistry Research
• 影响因子: 4.2
• 作者: Badejo, Oluwadare;Ierapetritou, Marianthi
• 通讯作者: Ierapetritou, Marianthi