GOALI: Stochastic Optimization Framework for Energy-Smart Re/Manufacturing Systems

GOALI：能源智能再造/制造系统的随机优化框架

• 批准号: 2038325
• 负责人: Faisal Aqlan
• 金额: $ 47.29万
• 依托单位: University of Louisville Research Foundation Inc
• 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: 2021-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038325&HistoricalAwards=false
• 关键词: GOALI; Stochastic; Optimization; Framework; Energy

This Grant Opportunities for Academic Liaison with Industry (GOALI) award will contribute to the national welfare by developing models to support the efficient integration of manufacturing and remanufacturing production lines. Remanufacturing is important to sustainable production by extending product life and reducing the environmental impact of manufacturing. Uncertain customer demand, along with highly variable product returns in both quantity and quality, have proved challenging to manufacturers in planning to allocate production capacity between new and returned products. The project, a collaboration between University of Louisville, Northeastern University, and IBM Corporation, will consider production scheduling and inventory levels, energy impact, uncertainty in demand, returns quantity, and returns quality to produce a production plan that is scalabile to industry-scale problems. The researched modeling approach is expected to inform the way such hybrid systems are designed, operated, and sustained, and will promote awareness of manufacturing-related e-waste considerations. The project will benefit US manufacturing by enabling the development of best practices for production-inventory management and recommendations for energy consumption and minimization of the energy footprint.This research will develop a novel three-stage stochastic optimization model that integrates tactical (production and energy) and operational (inventory) decisions under a single integrated framework. The third-stage operational decisions reflect three levels of uncertainty (demand, returns quantity, and returns quality). The second-stage, NP hard server-to-bank allocation problems (in the second stage) is addressed through a dual bin-packing model approach. The overall solution approach employs a scenario-based decomposition framework. A high-fidelity simulation model for the overall system will allow benchmarking of real-world strategies against solutions generated by the new approach. The industrial partner will pilot an implementation of the most promising policy from the benchmarking exercise, which will enable translation of the findings and fine-tuning of the approach. The project contributes to the training of next generation engineers via computational tools (e.g., optimization, virtual reality and simulation) and case studies to complement in-class instruction.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.

这种与行业联络的赠款机会（Goari）奖将通过开发模型来支持制造业和再制造生产线的有效整合，从而为国家福利做出贡献。 再制造对于可持续生产至关重要，通过延长产品寿命并减少制造业的环境影响。 不确定的客户需求以及数量和质量的高度可变产品回报，已证明对制造商计划在新产品和返回产品之间分配生产能力方面具有挑战性。 该项目是路易斯维尔大学，东北大学和IBM Corporation之间的合作，将考虑生产计划和库存水平，能源影响，需求不确定性，返回数量以及返回质量，以制定针对行业规模问题的生产计划。研究的建模方法有望为这种混合系统设计，操作和维持的方式提供信息，并将提高人们对制造相关的电子废物考虑的认识。该项目将通过实现最佳实践来开发用于生产的管理管理，并为能源足迹的最小化提供最佳实践，从而使我们受益。这项研究将开发出一种新型的三阶段随机优化模型，该模型在一个单一的集成框架下集成了战术（生产和能源）和运营（库存）和运营（库存）决策。第三阶段的操作决策反映了三个不确定性（需求，返回数量和返回质量）的级别。第二阶段的NP硬式服务器到银行分配问题（在第二阶段）通过双包装模型方法解决。总体解决方案方法采用基于方案的分解框架。整个系统的高保真模拟模型将允许对新方法产生的解决方案进行现实世界中的基准测试。工业合作伙伴将通过基准练习来实施最有前途的政策，这将使调查结果和微调对方法进行翻译。该项目通过计算工具（例如，优化，虚拟现实和模拟）和案例研究来培训下一代工程师，以补充课堂教学。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来获得支持的。