- AutorIn
- Daniel Merkle
- Martin Middendorf
- Titel
- On solving permutation scheduling problems with ant colony optimization
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-320515
- Quellenangabe
- International Journal of Systems Science Erscheinungsort: Milton Park [u.a.]
Verlag: Taylor & Francis Ltd.
Erscheinungsjahr: 2005
Jahrgang: 36
Heft: 5
Seiten: 255-266
ISSN: 0020-7721
E-ISSN: 1464-5319 - Erstveröffentlichung
- 2005
- Abstract (EN)
- A new approach for solving permutation scheduling problems with ant colony optimization (ACO) is proposed in this paper. The approach assumes that no precedence constraints between the jobs have to be fulfilled. It is tested with an ACO algorithm for the single-machine total weighted deviation problem. In the new approach the ants allocate the places in the schedule not sequentially, as in the standard approach, but in random order. This leads to a better utilization of the pheromone information. It is shown by experiments that adequate combinations between the standard approach which can profit from list scheduling heuristics and the new approach perform particularly well.
- Freie Schlagwörter (EN)
- Informatics, Ant algorithm, ACO, Permutation problems, Pheromone information
- Klassifikation (DDC)
- 004
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:15-qucosa2-320515
- Veröffentlichungsdatum Qucosa
- 26.10.2018
- Dokumenttyp
- Artikel
- Sprache des Dokumentes
- Englisch