Project Overview

It is no longer sufficient to simply optimise a nominal system design. Firstly systems are becoming increasingly complex, and non-linear in their behaviour, and hence more sensitive to variation and uncertainty. Indeed the optimisation process itself can lead designs into areas of solution space where the performance is high, but so is the sensitivity to variation. Secondly the emerging fundamental shift in many industries away from selling products to the provision of services is migrating the risk of new systems away from the purchaser of the product to the provider of the service. Commercial pressure is therefore increasing to address the paradoxical need for robust systems, which are insensitive to variation in their manufacture and operating environments, whilst also having the high levels of performance and innovation essential for competitiveness. Design Simulation and Modelling (DSM) is key on resolving this paradox.

The issue of integrating and optimising performance with reliability and risk will be the primary focus of this project named High Performance and Robust Systems (HIPARSYS). However, to fully resolve this paradox, and achieve reduced new production introduction time scales, DSM also needs to be extended to the processes and organisations that produce the systems. The organisation should be considered as part of the system, and itself needs to be made high performance and robust. The DSM of organisations will address the issue of predicting and validating the emergent behaviour of complex systems. The project's vision is to develop tools and methodologies that will achieve high performance and robust systems through relying on sophisticated and computationally expensive modelling and simulation.

Innovation within this project has two dimensions. The first lies in the integration and application of a number of disparate technologies that are currently in the research phase to a set of demanding real world problems. This will result in the further development and validation of these technologies. The second, and higher risk aspect, is extending DSM to organisational systems, by exploiting synergy between intelligent agent technology and the work psychology understanding of the operation of individuals and organisations. Further innovation is likely from tackling the issues of variability and uncertainly in this new domain of modelling organisations at the same time as addressing them in the more established world of modelling the physics of products.

The project has four work packages to address the key issues of building high performance and robust systems:

  • WP1 Computational Analysis will look into the parameterisation question together with the issues of minimising computational time;
  • WP2 Knowledge Capture will address how knowledge about manufacturing and environmental uncertainty should be captured, treated and reused;
  • WP3 Organisational Modelling will model organisations and processes;
  • WP4 Integration and Exploitation will ensure the integration and exploitation of the research outcomes among the industrial partners.

The whole project adopts a socio-technical approach, combining expertise in technical and social issues, which is essential for progress in these areas.