Multi-objective Flexible Job Shop Scheduling Problem Based on Monte-Carlo Tree Search

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.