Name
Abstract | ||
|---|---|---|
Flexible job shop scheduling problem (FJSP) is quite a difficult combinatorial model. Various metaheuristic algorithms are used to find a local or global optimum solution for this problem. Among these algorithms, variable neighborhood search (VNS) is a capable one and makes use of a systematic change of neighborhood structure for evading local optimum. The search process for finding a local or global optimum solution by VNS is totally random. This is one of the weaknesses of this algorithm. To remedy this weakness of VNS, this paper combines VNS algorithm with a knowledge module and proposes knowledge-based VNS (KBVNS). In KBVNS, the VNS part searches the solution space to find good solutions and knowledge module extracts the knowledge of good solution and feed it back to the algorithm. This would make the search process more efficient. Computational results of the paper on different size test problems prove the efficiency of our algorithm for FJS problem. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.knosys.2012.04.001 | Knowl.-Based Syst. |
Keywords | Field | DocType |
good solution,different size test problem,vns algorithm,global optimum solution,search process,knowledge module,vns part,fjs problem,solution space,efficient knowledge-based algorithm,flexible job shop scheduling,various metaheuristic algorithm | Mathematical optimization,Variable neighborhood search,Job shop scheduling problem,Computer science,Local optimum,Global optimum,Algorithm,Artificial intelligence,Local search (optimization),Combinatorial model,Machine learning,Metaheuristic | Journal |
Volume | ISSN | Citations |
36, | 0950-7051 | 23 |
PageRank | References | Authors |
0.78 | 23 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hamid Karimi | 1 | 31 | 1.92 |
| Seyed Habib A. Rahmati | 2 | 165 | 7.56 |
| M. Zandieh | 3 | 988 | 46.21 |