Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Computers and Operations Research
Some Observations about GA-Based Exam Timetabling
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
Variable neighborhood search for the linear ordering problem
Computers and Operations Research
An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The trade off between diversity and quality for multi-objective workforce scheduling
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
A variable neighbourhood search algorithm for job shop scheduling problems
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
Binary Exponential Back Off for Tabu Tenure in Hyperheuristics
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Improving metaheuristic performance by evolving a variable fitness function
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Days-off scheduling for a bus transportation company
International Journal of Innovative Computing and Applications
A Hyper-Heuristic Using GRASP with Path-Relinking: A Case Study of the Nurse Rostering Problem
Journal of Information Technology Research
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In this paper we study a complex real-world workforce scheduling problem. We propose a method of splitting the problem into smaller parts and solving each part using exhaustive search. These smaller parts comprise a combination of choosing a method to select a task to be scheduled and a method to allocate resources, including time, to the selected task. We use reduced Variable Neighbourhood Search (rVNS) and hyperheuristic approaches to decide which sub problems to tackle. The resulting methods are compared to local search and Genetic Algorithm approaches. Parallelisation is used to perform nearly one CPU-year of experiments. The results show that the new methods can produce results fitter than the Genetic Algorithm in less time and that they are far superior to any of their component techniques. The method used to split up the problem is generalisable and could be applied to a wide range of optimisation problems.