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.

该授予学术与工业联络机会（GOALI）奖将通过开发模型来支持制造和再制造生产线的高效整合，为国家福利做出贡献。 再制造通过延长产品寿命和减少制造对环境的影响对于可持续生产非常重要。 客户需求的不确定性，以及产品退货数量和质量的巨大差异，已证明对制造商在规划新产品和退货产品之间分配产能时具有挑战性。 该项目由路易斯维尔大学、东北大学和 IBM 公司合作，将考虑生产调度和库存水平、能源影响、需求不确定性、退货数量和退货质量，以制定可扩展到行业规模的生产计划问题。研究的建模方法预计将为此类混合系统的设计、操作和维持方式提供信息，并将提高人们对与制造相关的电子废物考虑因素的认识。该项目将通过开发生产库存管理的最佳实践以及能源消耗和能源足迹最小化的建议，使美国制造业受益。这项研究将开发一种新颖的三阶段随机优化模型，该模型集成了战术（生产和能源）和单一综合框架下的运营（库存）决策。第三阶段的运营决策反映了三个层面的不确定性（需求、退货数量和退货质量）。第二阶段，NP 硬服务器到银行分配问题（在第二阶段）通过双装箱模型方法解决。整体解决方案采用基于场景的分解框架。整个系统的高保真仿真模型将允许根据新方法生成的解决方案对现实世界的策略进行基准测试。行业合作伙伴将试点实施基准测试中最有前途的政策，这将有助于转化研究结果并微调方法。该项目通过计算工具（例如优化、虚拟现实和模拟）和案例研究来补充课堂教学，有助于培训下一代工程师。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。