His primary areas of study are Heuristics, Artificial intelligence, Hyper-heuristic, Mathematical optimization and Heuristic. His study in Heuristics is interdisciplinary in nature, drawing from both Scheduling and Search algorithm. Rong Qu combines subjects such as Iterative method and Machine learning with his study of Artificial intelligence.
His study in the field of Evolutionary computation also crosses realms of Class. In the field of Mathematical optimization, his study on Evolution strategy and Pareto principle overlaps with subjects such as Constructive, Portfolio optimization and Efficient frontier. His research in Heuristic intersects with topics in Quality, Vehicle routing problem, Decision support system and Incremental heuristic search.
Rong Qu mainly focuses on Mathematical optimization, Heuristics, Artificial intelligence, Heuristic and Benchmark. His studies examine the connections between Mathematical optimization and genetics, as well as such issues in Scheduling, with regards to Operations research and Nursing. Rong Qu usually deals with Heuristics and limits it to topics linked to Theoretical computer science and Evolutionary computation.
Rong Qu has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition. His Heuristic research is multidisciplinary, relying on both Quality, Hybrid algorithm, Selection, Hill climbing and Heuristic. The study incorporates disciplines such as Algorithm and Vehicle routing problem in addition to Benchmark.
Rong Qu mainly investigates Mathematical optimization, Heuristics, Heuristic, Benchmark and Artificial intelligence. His Mathematical optimization research is multidisciplinary, incorporating perspectives in Routing, Vehicle routing problem and Job shop scheduling. His studies in Heuristics integrate themes in fields like Combinatorial optimization problem, Theoretical computer science and Selection.
His research investigates the link between Heuristic and topics such as Hill climbing that cross with problems in Time horizon. His Benchmark research incorporates themes from Genetic algorithm and Fuzzy logic. His research integrates issues of Machine learning and Pattern recognition in his study of Artificial intelligence.
Mathematical optimization, Optimization problem, Heuristics, Benchmark and Evolutionary algorithm are his primary areas of study. His work on Multi-objective optimization as part of general Mathematical optimization study is frequently linked to Risk measure, Portfolio optimization and Post-modern portfolio theory, bridging the gap between disciplines. The concepts of his Optimization problem study are interwoven with issues in Multicast, Data transmission and Network packet, Linear network coding.
Rong Qu integrates Heuristics and Hyper-heuristic in his studies. His Benchmark study integrates concerns from other disciplines, such as Pixel, Image, Noise and Fuzzy logic. His Evolutionary algorithm study combines topics in areas such as Distributed computing, Wireless sensor network, Swarm behaviour, Decomposition and Service provider.
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Hyper-heuristics: a survey of the state of the art
Edmund K. Burke;Michel Gendreau;Matthew R. Hyde;Graham Kendall.
Journal of the Operational Research Society (2013)
A Graph-Based Hyper-Heuristic for Educational Timetabling Problems
Edmund K. Burke;Barry McCollum;Amnon Meisels;Sanja Petrovic.
European Journal of Operational Research (2007)
A Survey of Deep Learning-Based Object Detection
Licheng Jiao;Fan Zhang;Fang Liu;Shuyuan Yang.
IEEE Access (2019)
A survey of search methodologies and automated system development for examination timetabling
R. Qu;E. K. Burke;B. Mccollum;L. T. Merlot.
Journal of Scheduling (2009)
Case-based heuristic selection for timetabling problems
Edmund K. Burke;Sanja Petrovic;Rong Qu.
Journal of Scheduling (2006)
Setting the Research Agenda in Automated Timetabling: The Second International Timetabling Competition
Barry McCollum;Andrea Schaerf;Ben Paechter;Paul McMullan.
Informs Journal on Computing (2010)
A hybrid heuristic ordering and variable neighbourhood search for the nurse rostering problem
Edmund K. Burke;Timothy Curtois;Gerhard F. Post;Rong Qu.
European Journal of Operational Research (2008)
A hybrid model of integer programming and variable neighbourhood search for highly-constrained nurse rostering problems
Edmund K. Burke;Jingpeng Li;Rong Qu.
European Journal of Operational Research (2010)
Personnel scheduling: Models and complexity
Peter Brucker;Rong Qu;Edmund K. Burke.
European Journal of Operational Research (2011)
Hybrid variable neighbourhood approaches to university exam timetabling
Edmund Burke;Adam J Eckersley;Barry McCollum;Sanja Petrovic.
European Journal of Operational Research (2010)
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