Multi-objective flexible job shop scheduling problem based on monte-carlo tree search

Tung Ying Wu, I-Chen Wu, Chao Chin Liang

研究成果: Paper同行評審

9 引文 斯高帕斯(Scopus)

摘要

Flexible job-shop scheduling problem (FJSP) is very important in both fields of production management and combinatorial optimization. This paper focuses on the multiobjective flexible job shop scheduling problem (MO-FJSP) with three objectives which minimizing make span, total workload and maximal workload, respectively, with Pareto manner. In addition, Monte-Carlo Tree Search (MCTS) is successful in computer Go and many other games. Hence, solving FJSP by MCTS is a new attempt. In this paper, we propose an MCTS algorithm for FJSP, by incorporating Variable Neighborhood Descent Algorithm and other techniques like Rapid Action Value Estimates Heuristic and Transposition Table. Our algorithm finds Pareto solutions of the benchmark problems proposed by Kacem et al. within 116 seconds: 4 solutions in 4×5, 3 in 10×7, 4 in 8×8, 4 in 10×10 and 2 in 15×10. These solutions are the same as the best found to date. Although one article claimed to have an extra 8×8 solution, that article did not find some of the above solutions.

原文English
頁面73-78
頁數6
DOIs
出版狀態Published - 1 一月 2013
事件2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013 - Taipei, Taiwan
持續時間: 6 十二月 20138 十二月 2013

Conference

Conference2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013
國家/地區Taiwan
城市Taipei
期間6/12/138/12/13

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