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

该奖项将通过开发模型来支持制造和再制造生产线的有效整合，从而为国家福利做出贡献。 再制造通过延长产品寿命和减少制造对环境的影响，对可持续生产非常重要。 不确定的客户需求，沿着高度可变的产品退货数量和质量，已经证明对制造商在计划新产品和退货产品之间分配生产能力具有挑战性。 该项目是路易斯维尔大学，东北大学和IBM公司之间的合作，将考虑生产调度和库存水平，能源影响，需求的不确定性，退货数量和退货质量，以产生可扩展到行业规模问题的生产计划。研究的建模方法预计将告知这种混合系统的设计，操作和维持方式，并将促进对制造相关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最小化能源足迹的建议而使美国制造业受益。该研究将开发一种新颖的三阶段随机优化模型，该模型将战术（生产和能源）和运营（库存）决策集成在一个集成框架下。第三阶段的经营决策反映了三个层次的不确定性（需求，退货数量和退货质量）。第二阶段，NP硬服务器到银行的分配问题（在第二阶段）是通过双装箱模型的方法来解决。整体解决方案方法采用了一个基于XML的分解框架。整个系统的高保真仿真模型将允许对新方法产生的解决方案进行现实世界战略的基准测试。工业合作伙伴将试行实施基准制定工作中最有希望的政策，这将使调查结果得以转化，并对方法进行微调。该项目有助于通过计算工具（例如，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。

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.