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

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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.