Swarm intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Timetable Scheduling Using Particle Swarm Optimization
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 3
Staff Scheduling with Particle Swarm Optimisation and Evolution Strategies
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
A comparison of neighbourhood topologies for staff scheduling with particle swarm optimisation
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
| Hi-index | 0.00 |
Eight problems of a practical staff scheduling application from logistics are used to compare the effectiveness and efficiency of two fundamentally different solution approaches. One can be called centralized and is based on search in the solution space with an adapted metaheuristic, namely particle swarm optimization (PSO). The second approach is decentralized. Artificial agents negotiate to construct a staff schedule. Both approaches significantly outperform todays manual planning. PSO delivers the best overall results in terms of solution quality and is the method of choice, when CPU-time is not limited. The agent approach is vastly quicker in finding solutions of almost the same quality as PSO. The results suggest that agents could be an interesting method for real-time scheduling or re-scheduling tasks.