Ant Colony Optimization is a relatively new meta-heuristic that has proven its quality and versatility on various combinatorial optimization problems such as the traveling salesman problem, the vehicle routing problem and the job shop scheduling problem. The paper introduces an Ant Colony Optimization approach to solve the problem of determining a job-sequence that minimizes the overall tardiness for a given set of jobs to be processed on a single, continuously available machine, the Single Machine Total Tardiness Problem. We experiment with various heuristic information as well as with variants for local search. Experiments with 250 benchmark problems with 50 and 100 jobs illustrate that Ant Colony Optimization is an adequate method to tackle the SMTTP. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:epub-wu-01_1dd |
| Date | January 1999 |
| Creators | Bauer, Andreas, Bullnheimer, Bernd, Hartl, Richard F., StrauĂ, Christine |
| Publisher | SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Paper, NonPeerReviewed |
| Format | application/pdf |
| Relation | http://epub.wu.ac.at/1368/ |
Page generated in 0.0021 seconds