Dr. Fang He

The University of Nottingham                               
School of Computer Science                         
Campus in Wollaton Road                            
Nottingham NG8 1BB                                            
United Kingdom                                              

mailto:fxh@cs.nott.ac.uk


Research Interests

·         Operational research

·         Combinatorial optimisation

·         Constraint Programming

·         Local search methods

·         Stochastic programming


Education

Before Sep,2007: Msc, Bsc of Xi’an Jiaotong University, Xi’an, China

2012: Ph.D. of University of Nottingham, UK


Projects

Project title: Hybrid Solution Approach to Advanced Real-world Portfolio Optimisation Problems

Post-doctoral research project, Funded by LANCS innovation,University of Nottingham, U.K., Jul/2012-present

To take the uncertainty of market into account when optimising the portfolio, stochastic programming has been applied to model the uncertainty. Hybrid solution methods which integrate mathematic methods with heuristics have been investigated to seek high quality solutions in a reasonable computational time.

Project title: Towards Advanced Real-world Portfolio Optimisation

Post-doctoral research project, funded by EPSRC Research Development Fund - Pathways to Impact, University of Nottingham, U.K., Feb/2012-Jun/2012

Demands of NAG’s customers have been identified, understood and converted into mathematical constraints to model real world portfolio selection problems. Math-heuristics methods have been developed and used to generate qualitative measureable solution in a limited CPU time.

Project title: Integration of constraint programming, OR techniques and local search for combinatorial optimisation problems

Ph.D. project, University of Nottingham, U.K., Sep/2007-Mar/2011

Hybrid approaches to highly constrained large scale optimisation problems have been systematically investigated and designed. The approaches benefit from the efficiency of AI (Constraint Programming) in modelling and solving complex logical and non-linear constraints, and classic OR techniques, such as IP and LP. Constraint-directed local search has also been investigated in order to seek good results in limited computational time.


Publications

Journals

·         F. He, R. Qu, Integrated nurse scheduling optimisation with Conditional Value-at-Risk constraint under patient demand uncertainty, under review, 2014

·         F. He, R. Qu, R. John, A compromise based fuzzy goal programming approach with satisfaction function for multi-objective portfolio selection, under review, 2014

·         F. He, R. Qu, E. Tsang,  Hybridising local search with Branch-and-Bound for constrained portfolio selection problems, under review, 2013

·         F. He, R. Qu, A two-stage stochastic mixed-integer program modelling and hybrid solution approach to portfolio selection problems, accepted for publishing at Information Sciences, 2014. Impact factor: 3.643.pdf

·         F. He, R. Qu, A Constraint Programming based column generation approach to nurse rostering problems, Computers & Operations Research, 39(12): 3331–3343, 2012. Impact factor: 1.909.pdf

 

Refereed Conference papers

·         F. He, R. Qu, A Constraint-directed Local Search Approach to Nurse Rostering Problems, In Proceeding of the 6th International Workshop on  Local Search Techniques in Constraint Satisfaction (LSCS'09)  at the 15th International Conference on Principles and Practice of Constraint Programming (CP09), Lisbon, Portugal, 20 Sep, 2009. .pdf

·         R. Qu, F. He, E. K. Burke, Hybridizing Integer Programming Models with an  Adaptive Decomposition Approach for Exam Timetabling Problems, in: Proceeding of 4th Multidisciplinary International Scheduling Conference (MISTA2009), Dublin, Ireland, 10-12 August, 2009..pdf

·         R. Qu, F. He, A Hybrid Constraint Programming Approach for Nurse Rostering Problems, Allen T., Ellis R. and Petridis M. (eds.) Applications and Innovations in Intelligent Systems XVI.  The Twenty-eighth SGAI International Conference on Artificial Intelligence (AI-2008), 211-224, Cambridge, England, 9-11 December 2008. .pdf


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