An Efficient Genetic Algorithm for Job Shop Scheduling Problems
Proceedings of the 6th International Conference on Genetic Algorithms
A bee colony optimization algorithm to job shop scheduling
Proceedings of the 38th conference on Winter simulation
A survey of dynamic scheduling in manufacturing systems
Journal of Scheduling
An Improved Clonal Selection Algorithm for Job Shop Scheduling
IUCE '09 Proceedings of the 2009 International Symposium on Intelligent Ubiquitous Computing and Education
Stretching Technique-Based Clonal Selection Algorithm for Flexible Job-shop Scheduling
CINC '09 Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 02
International Journal of Bio-Inspired Computation
Cooperation of hybrid agents in models of manufacturing systems
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
Relationships of swarm intelligence and artificial immune system
International Journal of Bio-Inspired Computation
Ant colony optimisation for vehicle traffic systems: applications and challenges
International Journal of Bio-Inspired Computation
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The main task of the scheduling optimisation process in production systems is to minimise production cost, overall production time and to ensure optimal utilisation of the resources. Application of stochastic search techniques to find a feasible schedule that minimise cost and satisfy all constraints jointed with all products can bring a particular solution of the complexity problem. On the other hand, the cost and the time of an optimisation process have to reciprocate with the found schedule; otherwise the optimisation loses its meaning. The article presents two stochastic methods, based on biologically inspired techniques, applied on a scheduling optimisation process. The first one is based on the mechanism inspired by biological evolution and the one method applies the swarm intelligence. The application of methods is illustrated on a real world example of a production line.