Name
Abstract | ||
|---|---|---|
Optimising a train schedule on a single line track is known to beNP-Hard with respect to the number of conflicts in the schedule. Thismakes it difficult to determine optimum solutions to real lifeproblems in reasonable time and raises the need for good heuristictechniques. The heuristics applied and compared in this paper are alocal search heuristic with an improved neighbourhood structure,genetic algorithms, tabu search and two hybrid algorithms. When notime constraints are enforced on solution time, the genetic andhybrid algorithms were within five percent of the optimal solutionfor at least ninety percent of the test problems. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1023/A:1009672832658 | J. Heuristics |
Keywords | Field | DocType |
train scheduling,local search,tabu search,genetic algorithm,hybrid algorithm | Incremental heuristic search,Heuristic,Mathematical optimization,Guided Local Search,Computer science,Beam search,Heuristics,Artificial intelligence,Local search (optimization),Machine learning,Genetic algorithm,Tabu search | Journal |
Volume | Issue | ISSN |
3 | 1 | 1572-9397 |
Citations | PageRank | References |
42 | 4.80 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew J. Higgins | 1 | 129 | 17.59 |
| Erhan Kozan | 2 | 315 | 32.28 |
| Luis Ferreira | 3 | 58 | 8.72 |