In recent years, there has been a growing number of natural computing techniques, including genetic algorithms, neural networks, cellular automata and (hyper/meta)-heuristic methods for a variety of different scheduling problems. The aim of this workshop is to bring together researchers and practitioners to share their experiences and report on emerging approaches in solving real-world scheduling problems. A particular interest will be on approaches that give a deeper insight into scheduling problem classes, and that enable the exploitation of structural information during the automated search for a solution to a given problem. General purpose approaches used for automated generation of heuristics for solving single and multi-objective scheduling problems and issues related to development of such approaches are also of particular interest. Data science techniques improving heuristic optimisation is a growing area of research. Such novel applications or reflections on particular line of work in scheduling are also of interest.
Airport scheduling
Personnel rostering
Surgery scheduling
Production scheduling
Flow shop scheduling
Vehicle routing
Sports scheduling
Project scheduling
Real-time scheduling
Educational timetabling
Multi-objective scheduling problems
(Hyper/Meta)-heuristics for scheduling problems
Sep 18
2016
Conference Date
Draft paper submission deadline
Draft Paper Acceptance Notification
Registration deadline
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