Name
Abstract | ||
|---|---|---|
In this paper an Artificial Bee Colony Approach for Scheduling Optimization is presented. The adequacy of the proposed approach is validated on the minimization of the total weighted tardiness for a set of jobs to be processed on a single machine and on a set of instances for Job-Shop scheduling problem. The obtained computational results allowed concluding about their efficiency and effectiveness. The ABC performance and respective statistical significance was evaluated. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/NaBIC.2013.6617872 | Nature and Biologically Inspired Computing |
Keywords | Field | DocType |
artificial intelligence,computational complexity,job shop scheduling,minimisation,statistical analysis,ABC performance,NP-complete scheduling problems,artificial bee colony approach,job-shop scheduling problem,scheduling optimization,statistical significance,swarm intelligence,total weighted tardiness minimization,Artificial Bee Colony,Optimization,Scheduling,Self-organization,Swarm Intelligence | Mathematical optimization,Job shop scheduling,Tardiness,Scheduling (computing),Computer science,Flow shop scheduling,Nurse scheduling problem,Minimisation (psychology),Minification,Artificial intelligence,Machine learning,Computational complexity theory | Conference |
ISSN | ISBN | Citations |
2164-7364 | 978-1-4799-1414-2 | 1 |
PageRank | References | Authors |
0.36 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ana Maria Madureira | 1 | 30 | 5.54 |
| Ivo Pereira | 2 | 12 | 5.10 |
| Ajith Abraham | 3 | 8954 | 729.23 |