CAREER: LEarning to Search with Structure (LESS), a Unifying Algorithmic Framework for Gray Box Optimization of Biomanufacturing Systems

职业：学习结构搜索（LESS），生物制造系统灰盒优化的统一算法框架

• 批准号: 2046588
• 负责人: Giulia Pedrielli
• 金额: $ 51.04万
• 依托单位: Arizona State University
• 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: 2021-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046588&HistoricalAwards=false
• 关键词: CAREER; LEarning; Search; Structure; LESS

This Faculty Early Career Development Program (CAREER) grant will contribute to the advancement of national prosperity and economic welfare by studying efficient operations of single use scalable individualized manufacturing systems. Developing and manufacturing a new drug now often faces very tight deadlines, and a myriad of individual variants may be required to be produced. In such scenarios traditional large batch-production is generally poorly suited because of low flexibility in the quantity and type of drug being manufactured, large set up times between production runs and inability to distribute manufacturing capacity across locations and product types. This award supports better understanding of single use manufacturing as the fundamental enabler for renewed production flexibility in terms of both type variety and volume. This research will serve the biopharmaceutical manufacturing environment as well as the manufacturing community at large, promoting a new way to scale out instead of scaling up manufacturing systems. The accompanying educational plan aims to broaden STEM interest in simulation and, particularly, simulation based optimization aiming at the development of new tools for teaching and research, with a particular focus on creating a diverse research and educational ecosystem.This research will focus on the advancement of simulation based optimization methods to support decision making for the operation of individualized manufacturing systems. The project will result in new methods for the acceleration of black box optimization. The framework will consider the specific challenges of operating a large number of manufacturing processes at small scale, allowing to use the process simulation not only as a means to evaluate the performance, but also to provide structural properties of the process being operated. This research fills an important gap in the black box optimization literature, which reportedly suffers from poor finite time performance, only exploit output of the simulation model and ignores sample path information, and, finally, faces important hurdles in scaling to solve high dimensional problems. The analytical infrastructure leverages and extends state-of-the-art techniques from Bayesian optimization, high dimensional statistics, and model predictive control. The project will devise methods to efficiently achieve satisfactory solutions to simulation based optimization in presence of discontinuities resulting from the dynamics of the system. Finite time performance of the algorithms will be studied, and high dimensional problems will be central to the development of the techniques. The performance of the techniques will be evaluated not only using synthetic state of the art black box optimization problems, but using large scale production of a large variety of bio-products in an individualized manufacturing set-up. With the idea to promote this new idea of manufacturing and the concepts of simulation-supported decision making, a game will be implemented to attract students, as well as, potentially, practitioners, to the area of manufacturing and operations research.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.

该教师早期职业发展计划（CAREER）拨款将通过研究一次性可扩展个性化制造系统的有效运营，为国家繁荣和经济福利的发展做出贡献。现在，开发和制造一种新药往往面临非常紧迫的期限，可能需要生产无数的个体变体。在这种情况下，传统的大批量生产通常不太适合，因为所制造的药物的数量和类型的灵活性低，生产运行之间的设置时间长，并且无法跨地点和产品类型分配制造能力。该奖项支持更好地理解一次性制造作为在类型多样性和数量方面更新生产灵活性的基本推动力。这项研究将服务于生物制药制造环境以及整个制造业，促进一种新的方式来扩大规模，而不是扩大制造系统。伴随的教育计划旨在扩大STEM对仿真的兴趣，特别是基于仿真的优化，旨在开发新的教学和研究工具，特别关注创建多样化的研究和教育生态系统。这项研究将专注于基于仿真的优化方法的进步，以支持个性化制造系统的操作决策。该项目将产生加速黑盒优化的新方法。该框架将考虑在小规模下操作大量制造过程的具体挑战，允许使用过程模拟不仅作为评估性能的手段，而且还提供正在操作的过程的结构特性。这项研究填补了黑盒优化文献中的一个重要空白，据报道，黑盒优化文献有限时间性能差，只利用仿真模型的输出，忽略样本路径信息，最后，在解决高维问题时面临着重要的障碍。分析基础设施利用并扩展了贝叶斯优化、高维统计和模型预测控制等最先进的技术。该项目将设计方法，以有效地实现满意的解决方案，以模拟为基础的优化存在的不连续性所造成的系统的动态。有限的时间性能的算法将进行研究，高维问题将是中央的技术发展。这些技术的性能将不仅使用最先进的黑盒优化问题的合成状态进行评估，而且使用个性化制造设置中的各种生物产品的大规模生产进行评估。为了促进这种新的制造理念和模拟支持决策的概念，将实施一个游戏，以吸引学生，以及潜在的从业者，到制造和运营研究领域。该奖项反映了NSF的法定使命，并已被认为是值得支持的，通过评估使用基金会的智力价值和更广泛的影响审查标准。

项目成果

Gaussian Processes for High-Dimensional, Large Data Sets: A Review

• DOI: 10.1109/wsc57314.2022.10015416
• 发表时间: 2022-12
• 期刊: 2022 Winter Simulation Conference (WSC)
• 作者: Mengrui Jiang;Giulia Pedrielli;S. Ng

Efficient Optimization-Based Falsification of Cyber-Physical Systems with Multiple Conjunctive Requirements

• DOI: 10.1109/case49439.2021.9551474
• 发表时间: 2021-08
• 期刊: 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)
• 作者: L. Mathesen;Giulia Pedrielli;Georgios Fainekos

Demo Abstract: Analysing CPS Security with Falsification on the Microsoft Flight Simulator

演示摘要：在 Microsoft 飞行模拟器上通过伪造分析 CPS 安全性

• DOI: 10.1145/3575870.3589550
• 发表时间: 2023
• 期刊: HSCC 2023
• 作者: Khandait, Tanmay;Chandratre, Aniruddh;Baptista, Walstan;Pedrielli, Giulia;Fainekos, Georgios
• 通讯作者: Fainekos, Georgios

Using Gaussian Processes to Automate Probabilistic Branch & Bound for Global Optimization

使用高斯过程自动化概率分支

• DOI: 10.1109/case49439.2021.9551592
• 发表时间: 2021
• 期刊: IEEE CASE 2021
• 作者: Pedrielli, Giulia;Huang, Hao;Zabinsky, Zelda B.
• 通讯作者: Zabinsky, Zelda B.

Partitioning and Gaussian Processes for Accelerating Sampling in Monte Carlo Tree Search for Continuous Decisions

• DOI: 10.1109/wsc52266.2021.9715405
• 发表时间: 2021-12
• 期刊: 2021 Winter Simulation Conference (WSC)
• 作者: Menghan Liu;Giulia Pedrielli;Yumeng Cao