Monte-Carlo Tree Search for a reentrant scheduling problem

This paper reports application results of Monte-Carlo Tree Search (MCTS) for a practical problem. In this paper, a reentrant scheduling problem is considered as a practical problem which has been addressed by our previous works. MCTS introduced by Coulom is a best-first search where the pseudorandom simulations guide the solution of problem. Recent improvements on MCTS have produced strong computer Go program, which has a large search space, and the success is a hot topic for selecting the best move. So far, most of reports about MCTS have been on two-player game, and MCTS has been applied rarely in one-player perfect-information games. MCTS does not need an admissible heuristic, so the application of MCTS for one-player games might be an interesting alternative. Additionally, one-player games like puzzles are determinately operated only by one player's decision, so sequences of changes in state are describable as a network diagram of interdependence of operations. Therefore if MCTS for one-player games is considered as a meta-heuristic algorithm, we can use this algorithm for not only many practical problems, but also combinatorial optimization problems. Especially as MCTS does not fully depend on evaluation function, so the solutions based on MCTS remain effective if objective function of problem is modified. This paper firstly investigated on the application of Single Player MCTS (SP-MCTS) introduced by Schadd et al. Next this paper showed the effectiveness of new simulation strategies on SP-MCTS by numerical experiments. Based on the results, this paper discussed the application potentiality of SP-MCTS for a practical reentrant scheduling problem.