Computers and Operations Research
Intensification and diversification with elite tabu search solutions for the linear ordering problem
Computers and Operations Research
Ethernet: distributed packet switching for local computer networks
Communications of the ACM
A Hyperheuristic Approach to Scheduling a Sales Summit
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
Performance analysis of exponential backoff
IEEE/ACM Transactions on Networking (TON)
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
Exact/heuristic hybrids using rVNS and hyperheuristics for workforce scheduling
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
Choosing the fittest subset of low level heuristics in a hyperheuristic framework
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
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 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 propose a new tabu search hyperheuristic which makes individual low level heuristics tabu dynamically using an analogy with the Binary Exponential Back Off (BEBO) method used in network communication. We compare this method to a reduced Variable Neighbourhood Search (rVNS), greedy and random hyperheuristic approaches and other tabu search based heuristics for a complex real world workforce scheduling problem. Parallelisation is used to perform nearly 155 CPU-days of experiments. The results show that the new methods can produce results fitter than rVNS methods and within 99% of the fitness of those produced by a highly CPU-intensive greedy hyperheuristic in a fraction of the time.