Towards More Effective Computational Search

Lead Research Organisation: University of Nottingham
Department Name: School of Computer Science

Abstract

The ASAP group has set the international research agenda in exploring the development of computational systems that can automatically build decision support systems. The group addresses a broad range of scientifically challenging problems, many of which are drawn from the real world where the complexities of the problem have not been abstracted away in order to make the problem easier to model/solve.The group's key research goals include:- Automating the Heuristic Design Process: We lead the international community in hyper-heuristics (heuristics to choose heuristics) research, with the aim being to investigate the extent to which we can replace human decision making by computer algorithms.- Closing the gap between industrial and real world issues and academic practice: We aim to explore dynamic and complex computational modeling and intelligent decision support within the context of real world problems such as aircraft scheduling, timetabling, manufacturing, bioinformatics, production scheduling and healthcare rostering. ASAP aims to establish new decision support methodologies that explore the use of automated search methodologies and the complexity that they are able to handle.- Closing the gap between theoretical understanding and the construction of search methodologies: We aim to theoretically analyse complex real world scenarios with a view to deepening our understanding of search methodology development. The state of the art in theoretical study in this field tends to deal with models that are too simple to be placed into real world practice. We aim to study the theory of real world applications.Our core research on modeling and search methodologies has redefined the inter-disciplinary interface between Computer Science and Operational Research, while our grounding in diverse applications involves dialogue with many other disciplines spanning biomedical science (new computational methodologies in bioinformatics, systems and synthetic biology as well as in nanoscience) through to the built environment (search methodologies for office space allocation). In this renewal proposal to our current Platform award, we are requesting support for 132 months of research assistant funding (at varying levels of seniority), over a five year period. This would enable us to conduct (and continue) a programme of transformative and innovative research that is not only high risk and high return, but which also has a clear multi-disciplinary and industrial focus.A Platform award would enable the ASAP group to retain key personnel at the interface of Computer Science and Operational Research. The potential benefits in providing the grounding for tomorrow's decision support systems could be far reaching in laying the foundations for more efficient, effective, cheaper, easier-to-implement and easier-to-use systems across many industries and sectors.

Publications

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Karapetyan D (2019) Pattern-Based Approach to the Workflow Satisfiability Problem with User-Independent Constraints in Journal of Artificial Intelligence Research

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De Maere G (2018) Pruning Rules for Optimal Runway Sequencing in Transportation Science

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Li J (2018) On Nie-Tan Operator and Type-Reduction of Interval Type-2 Fuzzy Sets in IEEE Transactions on Fuzzy Systems

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Karapetyan D (2017) Markov Chain methods for the Bipartite Boolean Quadratic Programming Problem in European Journal of Operational Research

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Xing H (2017) A path-oriented encoding evolutionary algorithm for network coding resource minimization in Journal of the Operational Research Society

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Li J (2017) A Hyperheuristic Methodology to Generate Adaptive Strategies for Games in IEEE Transactions on Computational Intelligence and AI in Games

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Burke E (2017) Hyper-heuristics: a survey of the state of the art in Journal of the Operational Research Society

 
Description The optimisation research conducted on this grant have informed a number of further activities. In particular the research group has applied for and received funding to carry out optimisation R&D in the logistics field. We received significant funding from Innovate UK to develop optimisation systems to inform driver behaviour. We have also received funding for Knowledge Transfer Partnerships in optimisation which was aided by the research funded here. We also received funding for optimisation of factory performance from the EU - again informed by the research here.
Exploitation Route Optimisation and data science are growing areas in all industries and the numerous research papers that have appeared from this grant will help these organisations.
Sectors Construction,Digital/Communication/Information Technologies (including Software),Environment,Financial Services, and Management Consultancy,Healthcare,Manufacturing, including Industrial Biotechology,Transport