Name
Abstract | ||
|---|---|---|
This paper presents ACO_GLS, a hybrid ant colony optimization approach coupled with a guided local search, applied to a layout problem. ACO_GLS is applied to an industrial case, in a train maintenance facility of the French railway system (SNCF). Results show that an improvement of near 20% is achieved with respect to the actual layout. Since the problem is modeled as a quadratic assignment problem (QAP), we compared our approach with some of the best heuristics available for this problem. Experimental results show that ACO_GLS performs better for small instances, while its performance is still satisfactory for large instances. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.ejor.2006.10.032 | European Journal of Operational Research |
Keywords | Field | DocType |
Layout problem,Quadratic assignment problem,Ant colony optimization,Guided local search | Ant colony optimization algorithms,Mathematical optimization,Guided Local Search,Quadratic assignment problem,Swarm intelligence,Assignment problem,Heuristics,Artificial intelligence,Quadratic programming,Local search (optimization),Mathematics,Operations management | Journal |
Volume | Issue | ISSN |
183 | 2 | 0377-2217 |
Citations | PageRank | References |
24 | 1.31 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. Hani | 1 | 30 | 1.88 |
| Lionel Amodeo | 2 | 325 | 26.83 |
| Farouk Yalaoui | 3 | 481 | 42.61 |
| Haoxun Chen | 4 | 773 | 60.23 |