Optimization for robust design: Integrating model-based systems engineering with multi-criteria decision-making support in a distributed framework

稳健设计的优化：在分布式框架中集成基于模型的系统工程与多标准决策支持

• 批准号: EP/L025760/1
• 负责人: Peter Fleming
• 金额: $ 136.9万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FL025760%2F1
• 关键词: Optimization; robust; design; Integrating; model

Complex manufactured products, such as automotive vehicles, involve the design and integration of a large number of engineered components that interact to produce the overall qualities of the product. For example, the overall CO2 emissions from a vehicle depend on the design of the engine, transmission and exhaust systems, all working together. Usually different teams of designers work on the different parts of the vehicle, separated potentially by different physical locations, different design timescales, and the expertise and language of different scientific disciplines. These differences sometimes mean that costly re-work is required to successfully resolve incompatible or undesirable choices made for particular parts of the design. This project aims to develop methods that support these design teams in developing high quality designs for the overall advanced product (e.g. the full vehicle), and in choosing designs that will actually perform well in practice - once they have been manufactured and delivered to customers. The methods will be piloted using two case studies of vehicle design for Jaguar Land Rover and the software that is developed during the project will be made available as open-source, for everyone to use.The research will build on existing knowledge in three related academic disciplines: multi-objective optimization (which uses search methods to identify and reveal to designers the trade-offs between different aspects of product performance), multiple criteria decision-making (which helps designers to choose a single preferred design from amongst the trade-off options) and multidisciplinary optimization (which helps organize and integrate the search for good solutions between different design teams). The work will also use results from, and develop further, an on-going related research project that aims to understand the relationships between the different parts of a complex manufactured product and to identify computer models that can be used to inform the search for good solutions.The search for good designs that perform well in practice is known as "robust optimization". The research will develop methods for robust optimization at the level of a design "node" - that is, at the level of an individual component or sub-system within the overall product. The methods will make efficient use of the computing resources available for searching for good designs and will optimize multiple aspects of product performance simultaneously. Any patterns in the relationship between different design choices and different possible performance trade-offs will be identified from the data generated during the search and presented to the designer to help with choosing a single preferred design.The research will capture the impact of alternative design choices at one node on design activities at other nodes. The research then aims to use this information to steer robust optimization at different nodes, simultaneously, towards a favourable design for the overall product. Protocols will be developed, using methods from the academic discipline of systems engineering, that can highlight to design teams when decisions being taken elsewhere in the overall product design process are impacting on particular product requirements or resolving trade-offs unfavourably for aspects of performance relevant to the team.Successful delivery of the research objectives will represent a step-change in the ability of optimization and decision support tools to support the design teams working within the complex arrangement of engineering design processes that are a crucial feature of modern advanced manufactured products. Successful testing of the methods on real automotive design problems of strategic interest to Jaguar Land Rover will help to demonstrate the value of these methods to advanced manufacturing more generally in the UK, across a wide range of sectors.

复杂的制造产品（例如汽车车辆）涉及大量工程组件的设计和集成，这些组件相互作用以产生产品的整体品质。例如，车辆的总二氧化碳排放量取决于发动机，变速箱和排气系统的设计。通常，不同的设计师团队在车辆的不同部位工作，可能会通过不同的物理位置，不同的设计时间表以及不同科学学科的专业知识和语言分开。这些差异有时意味着需要昂贵的重新工作才能成功解决针对设计的特定部分的不兼容或不良选择。该项目旨在开发支持这些设计团队为整体高级产品（例如完整车辆）开发高质量设计的方法，并选择在实践中实际上表现良好的设计 - 一旦制造并交付给客户。 The methods will be piloted using two case studies of vehicle design for Jaguar Land Rover and the software that is developed during the project will be made available as open-source, for everyone to use.The research will build on existing knowledge in three related academic disciplines: multi-objective optimization (which uses search methods to identify and reveal to designers the trade-offs between different aspects of product performance), multiple criteria decision-making (which helps designers to choose a single preferred design from amongst the权衡选项）和多学科优化（这有助于组织和整合不同设计团队之间的良好解决方案的搜索）。这项工作还将使用一个正在进行的相关研究项目的结果并进一步发展，该项目旨在了解复杂制造产品的不同部分之间的关​​系，并确定可用于搜索良好解决方案的计算机模型。在实践中表现良好的良好设计搜索被称为“强大优化”。该研究将开发在设计“节点”级别上进行鲁棒优化的方法，即在整个产品中的单个组件或子系统的级别上。这些方法将有效利用可用于搜索良好设计的计算资源，并将同时优化产品性能的多个方面。不同的设计选择和不同可能的性能权衡之间关系中的任何模式将从搜索过程中生成的数据中确定，并向设计师呈现，以帮助选择单个首选设计。研究将捕获一个节点对其他节点的设计活动的替代设计选择的影响。然后，该研究旨在使用这些信息来同时对不同节点进行强大的优化，以实现整体产品的有利设计。将使用系统工程学术纪律的方法制定协议，当决策在整个产品设计过程中的其他位置都在对特定产品需求或解决绩效方面的不利方面不利的折衷影响时，这些方法可以强调设计团队。对团队的绩效方面不利。实现的研究目标将代表一项在设计方面的交付，以支持一定能力，以支持一定的设计工具，以实现设计工具，以使设计工具具有优化工具，以实现设计的能力，以实现设计工具，以实现设计的能力，以实现设计工具，以实现设计工具，以实现设计工具，以实现设计工具，以实现设计工具的能力。现代高级制造产品的关键特征。成功地测试了捷豹路虎战略兴趣的实际汽车设计问题的方法，将有助于证明这些方法在英国更广泛地在广泛的领域中更广泛地进行高级制造的价值。

项目成果

Collaborative multi-objective optimization for distributed design of complex products

• DOI: 10.1145/3205455.3205579
• 发表时间: 2018-07
• 期刊: Proceedings of the Genetic and Evolutionary Computation Conference
• 作者: João A. Duro;Yiming Yan;R. Purshouse;P. Fleming

Methods for multi-objective optimization: An analysis

• DOI: 10.1016/j.ins.2014.08.071
• 发表时间: 2015-02-01
• 期刊: INFORMATION SCIENCES
• 影响因子: 8.1
• 作者: Giagkiozis, I.;Fleming, P. J.
• 通讯作者: Fleming, P. J.

Generalized decomposition and cross entropy methods for many-objective optimization

• DOI: 10.1016/j.ins.2014.05.045
• 发表时间: 2014-10
• 期刊: Inf. Sci.
• 作者: I. Giagkiozis;R. Purshouse;P. Fleming

Component-based design of multi-objective evolutionary algorithms using the Tigon optimization library

• DOI: 10.1145/3449726.3463194
• 发表时间: 2021-07
• 期刊: Proceedings of the Genetic and Evolutionary Computation Conference Companion
• 作者: João A. Duro;Daniel C. Oara;Ambuj Sriwastava;Yiming Yan;Shaul Salomon;R. Purshouse

Expression analysis and regulation of GLI and its correlation with stemness and metabolic alteration in human brain tumor.

GLI在人脑肿瘤中的表达分析和调控及其与干性和代谢改变的相关性。

• DOI: 10.1007/978-3-319-30936-1_10
• 发表时间: 2023
• 期刊: 3 Biotech
• 影响因子: 2.8
• 作者: Agrawal K