Name
Abstract | ||
|---|---|---|
Multi-objective flexible job-shop scheduling problem (MO-FJSP) is very important in both fields of production management and combinatorial optimization. Wu et al. proposed a Monte-Carlo Tree Search (MCTS) to solve MO-FJSP and successfully improved the performance of MCTS to find 17 Pareto solutions: 4 of Kacem 4×5, 3 of 10×7, 4 of 8×8, 4 of 10×10, and 2 of 15×10. This paper proposes a new MCTS-based algorithm for MO-FJSP problem by modifying their algorithm. Our experimental results show that our new algorithm significantly outperforms their algorithm for large problems, especially for Kacem 15×10. This shows that the new algorithm tends to have better potential of solving harder MO-FJSP problems. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TAAI.2015.7407061 | 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI) |
Keywords | Field | DocType |
Monte-Carlo Tree Search,Multi-Objective Flexible Job Shop Scheduling Problem,Evolutionary Algorithm,Rapid Action Value Estimates | Production manager,Monte Carlo tree search,Mathematical optimization,Job shop scheduling,Evolutionary algorithm,Job shop scheduling problem,Computer science,Flow shop scheduling,Algorithm,Combinatorial optimization,Pareto principle | Conference |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jen-Jai Chou | 1 | 0 | 0.34 |
| Chao-Chin Liang | 2 | 13 | 3.36 |
| Hung-Chun Wu | 3 | 3 | 1.12 |
| I-Chen Wu | 4 | 208 | 55.03 |
| Tung-Ying Wu | 5 | 2 | 0.72 |