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，涉及大量工程部件的设计和集成，这些部件相互作用以产生产品的整体质量。例如，车辆的二氧化碳总排放量取决于发动机、变速箱和排气系统的设计，所有这些都协同工作。通常，不同的设计师团队负责车辆的不同部分，这些部分可能因不同的物理位置、不同的设计时间尺度以及不同科学学科的专业知识和语言而分开。这些差异有时意味着需要昂贵的返工才能成功解决针对设计特定部分做出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先进产品（例如整车）开发高质量设计的方法，并选择在制造并交付给客户后在实践中实际表现良好的设计。这些方法将通过捷豹路虎车辆设计的两个案例研究进行试点，项目期间开发的软件将作为开源提供给所有人使用。该研究将建立在三个相关学科的现有知识的基础上：多目标优化（使用搜索方法来识别并向设计人员揭示产品性能不同方面之间的权衡）、多标准决策（帮助设计人员从 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对良好解决方案的搜索）。这项工作还将利用正在进行的相关研究项目的结果并进一步发展该项目，该项目旨在了解复杂制造产品不同部分之间的关​​系，并识别可用于为寻找良好解决方案提供信息的计算机模型。寻找在实践中表现良好的良好设计被称为“鲁棒优化”。该研究将开发在设计“节点”级别（即整个产品中的单个组件或子系统级别）进行稳健优化的方法。这些方法将有效利用可用于搜索良好设计的计算资源，并同时优化产品性能的多个方面。不同设计选择和不同可能的性能权衡之间关系的任何模式都将从搜索过程中生成的数据中识别出来，并呈现给设计人员，以帮助选择单个首选设计。该研究将捕获一个节点的替代设计选择对其他节点的设计活动的影响。然后，该研究旨在利用这些信息同时引导不同节点的稳健优化，从而为整个产品提供有利的设计。将使用系统工程学科的方法来开发协议，当整个产品设计过程中其他地方做出的决策影响特定产品需求或解决对与团队相关的性能方面不利的权衡时，可以向设计团队强调。研究目标的成功实现将代表优化和决策支持工具能力的逐步变化，以支持在复杂的工程设计过程安排中工作的设计团队。 现代先进制造产品的重要特征。在捷豹路虎具有战略意义的实际汽车设计问题上成功测试这些方法将有助于证明这些方法对英国更广泛的行业的先进制造的价值。

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.

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

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

Liger: A cross-platform open-source integrated optimization and decision-making environment

• DOI: 10.1016/j.asoc.2020.106851
• 发表时间: 2020-11
• 期刊: Appl. Soft Comput.
• 作者: João A. Duro;Yiming Yan;I. Giagkiozis;S. Giagkiozis;Shaul Salomon;Shaul Salomon;Daniel C. Oara