Name
Abstract | ||
|---|---|---|
Scheduling in flexible manufacturing systems (FMS) must take account of the shorter lead-time, the multiprocessing environment, the flexibility of alternative workstations with different processing times, and the dynamically changing states. The best scheduling approach, as described here, is to minimize makespan t M, total flow time t F, and total tardiness penalty p T. However, in the case of manufacturing system problems, it is difficult for those with traditional optimization techniques to cope with this. This article presents a new flow network-based hybrid genetic algorithm (hGA) approach for generating static schedules in a FMS environment. The proposed method is combined with the neighborhood search technique in a mutation operation to improve the solution of the FMS problem, and to enhance the performance of the genetic search process. We update the change in swap mutation and the local search-based mutation ration. Numerical experiments show that the proposed flow network-based hGA is both effective and efficient for FMS problems. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/s10015-004-0291-y | Artificial Life and Robotics |
Keywords | DocType | Volume |
flexible manufacturing systems fms · ge- netic algorithms ga · scheduling,genetics,local search | Journal | 8 |
Issue | Citations | PageRank |
1 | 5 | 0.70 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kwan-woo Kim | 1 | 87 | 10.47 |
| Genji Yamazaki | 2 | 88 | 12.04 |
| Lin Lin | 3 | 71 | 7.90 |
| Mitsuo Gen | 4 | 1873 | 130.43 |