ADOPT - Advancing optimisation technologies through international collaboration

当今社会的复杂、相互联系和快速变化的性质对决策者提出了越来越大的挑战。流程行业(石油和天然气、化学品、个人护理产品、食品、制药和农用化学品)竞争加剧，意味着必须将敏捷性融入流程设计和操作。此外，需要确保整个供应链的可靠性，将资源使用和环境影响降至最低，并最大限度地提高能源效率，这使得投资和运营决策变得尤为困难。长期以来，这种多方面的决策一直得到物理和工程过程的详细数学模型的帮助，这些过程实现了数字孪生，构成了智能制造技术和未来工业4.0的基石。但到目前为止，由于缺乏工具来利用这些模型，这些模型提供的全部好处一直受到阻碍，这些工具的开发超出了“如果会怎样？”情景分析。特别是，这些问题的大规模、非线性和不确定性阻碍了对基于优化的决策的理解，这些问题往往导致次优甚至非物理解。在萨金特过程系统工程中心(CPSE)和JARA模拟和数据科学中心(JARA-CSD)之间的采用合作中，我们建议通过开发改进的确定性全局优化方法来解决其中的一些缺陷，这是一类依赖于完整搜索技术的优化方法，并提供了一个严格的概念框架来克服局部优化的限制。我们的关键研究假设是，确定性全局优化与代理(简化)模型和机器学习的集成将使我们处理复杂决策问题的能力发生变革性变化，导致具有全局最优性证书的更易处理的解决方案，并提高对不确定性的弹性。这个新领域带来了以下具体的研究挑战，我们将在APPLE内解决：-识别一流的理论/算法全球优化框架和代理建模范例，以支持基于代理的优化；-处理链中的不确定性，将物理/模拟数据连接到代理模型和优化结果；以及-为混合整数非线性规划以外的更具挑战性的问题开发定制的确定性全局优化方法。APPLE协作将确定性全局优化领域的两个世界级研究人员团队和团队成员聚集在一起，他们是处理不确定性、解决大规模组合问题和将优化应用于现实世界工程问题的专家。此外，我们聚集的团队与著名的优化软件和过程建模公司合作，以增加研究成果的可及性并促进其传播。采用合作通过两个中心的科学专业知识的综合实力、可结合的软件基础设施的广度、其丰富的人力资本、其产业关系的覆盖范围以及建立长期合作伙伴关系的特殊潜力来创造附加值。它将导致科学进步，可以迅速在实际问题上进行测试，确保研究产生最大影响。

The complex, interconnected and fast-changing nature of today's society presents a growing challenge for decision-makers. Increased competition in the process industries (oil and gas, chemicals, personal care products, food, pharmaceuticals and agrochemicals) means that agility must be built into process design and operation. Furthermore, the need to ensure reliability across the supply chain, minimise resource use and environmental impact, and maximise energy efficiency combine to make investment and operational decisions especially difficult. Such multifaceted decision-making has long been aided by detailed mathematical models of physical and engineered processes, which enable digital twins and constitute a cornerstone of smart manufacturing technologies and the future Industry 4.0. But the full benefits afforded by these models have so far been hampered by the lack of tools for exploiting them beyond "what if?" scenario analysis. In particular, the uptake of optimisation-based decision-making has been hindered by the large-scale, nonlinear and uncertain nature of these problems that often leads to suboptimal or even unphysical solutions.In the ADOPT collaboration between the Sargent Centre for Process Systems Engineering (CPSE) and the JARA Center for Simulation and Data Science (JARA-CSD), we propose to address some of these shortcomings by developing improved methods for deterministic global optimisation, a class of optimisation methods that rely on complete search techniques and offer a rigorous conceptual framework to overcome the caveats of local optimisation. Our key research hypothesis is that the integration of deterministic global optimisation with surrogate (simplified) models and machine learning will enable transformational changes in our capability to tackle complex decision-making problems, leading to more tractable solutions with global optimality certificates and improved resilience to uncertainty. This nascent area brings about the following specific research challenges that we shall tackle within ADOPT:- identifying best-in-class theoretical / algorithmic global optimisation frameworks and surrogate modelling paradigms to empower surrogate-based optimisation;- handling uncertainty within the chain linking physical/simulated data to surrogate models and to optimisation results; and- developing bespoke deterministic global optimisation approaches for more challenging classes of problems beyond mixed-integer nonlinear programming.The ADOPT collaboration brings together two world-class teams of researchers in the field of deterministic global optimisation as well as team members who are specialists in handling uncertainty, in solving large-scale combinatorial problems, and in applying optimisation to real-world engineering problems. Furthermore, our assembled team partners with prominent optimisation software and process modelling companies in order to increase the accessibility of the research outputs and facilitate their dissemination.The ADOPT collaboration creates added-value through the combined strength of scientific expertise of the two centres, the breadth of the software infrastructure that can be brought together, the wealth of its human capital, the reach of its industrial relationships and the exceptional potential to establish a long-term partnership. It will lead to scientific advances that can be tested on practical problems quickly, ensuring maximum impact from the research.