[1G4-OS-22a] シミュレーションとAI(1/2)

[1G4-OS-22a-04] Frontiers of operation optimization enabled by reinforcement learning

〇Shumpei Kubosawa^1,2, Takashi Onishi^1,2, Yoshimasa Tsuruoka^1,3 (1. National Institute of Advanced Industrial Science and Technology, 2. NEC Corporation, 3. The University of Tokyo)

[[Online]]

Keywords:reinforcement learning, operations research, optimal control, scheduling

The automation and optimization of planning tasks, such as resource allocation and operational planning of various systems such as transportation systems and production facilities have been addressed mainly in operations research and each specific field. Conventionally, planning problems i.e. scheduling problems are reduced to combinatorial optimization problems and addressed using their solvers. In such cases, scheduling complex systems for a long period might incur combinatorial explosions and would be difficult to obtain the solution. Several scheduling problems can also be regarded as optimal control problems. Optimal control problems include several problems concerning sequential decision making such as board games. Reinforcement learning is a method to address them, and its recent advancement is significant. If the complex scheduling problems are reduced to optimal control problems i.e. deciding resource allocation at each time step, not as a whole, recent powerful reinforcement learning can be leveraged to obtain solutions in a short period after the training. In this paper, we introduce these perspectives and their practical applications including railway scheduling and chemical plant operation.