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Research in the HWCS Intelligent Systems Lab since 2006 has developed approaches to accelerate and improve large-scale optimization. This has led to new algorithms that enable multiple high-quality solutions for complex problems, either more quickly, with better solution quality than previously obtainable, or both. These algorithms, combined with uncertainty quantification techniques from related research, have been adopted by both British Petroleum Plc (BP) and Epistemy Ltd (an SME serving the oil/gas sector). Impact for BP includes improved business decision-making (relating to ~$330M in turnover),and impact for Epistemy includes sales of £230k.
Effective industrial design and simulation require efficient and versatile computing systems. As a result of research performed by our team experienced in High Performance Computing (HPC), novel software structures and aligned hardware architectures have led to significant benefits to the energy supply industry and to microprocessor manufacturers.
As a result of our research with supercomputing, simulation times for electric field patterns in power components have reduced more than 30-fold, with accurate complex 3-D outputs for an increased range of configurations, thereby enabling our partner company to achieve results not possible with commercial software and to reduce product development costs by $0.5M - $5M p.a.
Our research has been incorporated by Intel into their numerical libraries and now made available to the general public supported by their latest processor architectures. Intel now has a 82% share of processors, according to the November 2013 Top500 list.
The application of advanced control algorithms has generated an impact on the economy and the environment through increased precision and reduced cost of operation of fast mechanical systems. A reduction in fuel consumption and CO2 emissions has been achieved in the transportation industry by the implementation of novel advanced control algorithms for advanced cruise control systems.
Many operations in daily life, from manufacturing to running a hospital, need to optimise the return on use of resources where volume and value are conditions. Scheduling theory tackles some of the hardest practical optimisation problems, not known to be solvable in reasonable computation time. Strusevich and Kellerer have been able to reformulate practical scheduling challenges as `knapsack problems' - dealing with volume and value constraints - and then design approximation algorithms which can be applied back to the original challenge. The work has attracted EPSRC funding, stimulated a new field of research which is developing fast, been widely published, led to presentations at international conferences including the 2009 Computers and Industrial Engineering conference attended by industry practitioners and is impacting on Combinatorial Optimisation research.
Through close collaboration with scientists at the European Space Agency (ESA), research at the University of Southampton has developed new algorithms and an associated software tool that have contributed to more efficient spacecraft design. Now a standard component of the ESA's design technology, the tools have doubled the speed in which crucial design processes can be completed, resulting in increased efficiency over the REF period of 20 person-years — equivalent to €1 million in monetary terms — and maintaining the ESA's manufacturing competitiveness. The success of this work led to a €480,000 EU grant to adapt the tools for the avionics industry as part of efforts to meet ambitious environmental targets under the EU Clean Sky Initiative.
Optimisation tools developed in the UoA have significantly advanced the ability to find the best designs for complex systems in cases where these were previously unobtainable. These optimisation tools have been implemented in several companies to shorten design times, reduce costs and reduce CO2 emissions. This has brought about new multi-million pound revenues, long-term contracts, increased employment and contribution to sustainability targets.
Many organisations rely on increasingly large and complex datasets to inform operational decision- making. To assist decision-makers when decisions are data-driven, computational tools are needed that present reliable summary information and suggest options allied to the key objectives of decision-making. Research at RGU has developed novel learning and optimisation algorithms driven by multifactorial data and implemented this in commercial decision-support software. The research has had economic impact by providing products to be sold: drilling rig selection tool (ODS-Petrodata Ltd.) and subsea hydraulics diagnostic tool (Viper Subsea Ltd.). Further economic impact comes from operations management software developed for British Telecom.
The Computational Optimization Group (COG) in the Department of Computing produced new models, algorithms, and approximations for supporting confident decision-making under uncertainty — when computational alternatives are scarce or unavailable. The impact of this research is exemplified by the following: