Name
Abstract | ||
|---|---|---|
A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed at reasonable run-time costs. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1162/evco.1999.7.1.1 | Evolutionary Computation |
Keywords | Field | DocType |
Genetic algorithm,dynamic scheduling,job shop scheduling problem,permutation representation,tunable decoding | Mathematical optimization,Job shop scheduling,Fair-share scheduling,Computer science,Flow shop scheduling,Two-level scheduling,Least slack time scheduling,Rate-monotonic scheduling,Dynamic priority scheduling,Earliest deadline first scheduling,Distributed computing | Journal |
Volume | Issue | ISSN |
7 | 1 | 1063-6560 |
Citations | PageRank | References |
96 | 8.12 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Bierwirth | 1 | 586 | 38.75 |
| Dirk C. Mattfeld | 2 | 96 | 8.12 |