Solving Integrated Process Planning and Scheduling Problem via Graph Neural Network Based Deep Reinforcement Learning
This addresses the challenge of balancing solution quality and speed in manufacturing scheduling, though it appears incremental as it applies existing DRL and GNN techniques to a specific domain problem.
The paper tackled the Integrated Process Planning and Scheduling (IPPS) problem in manufacturing by proposing a novel end-to-end Deep Reinforcement Learning method using a Heterogeneous Graph Neural Network and Proximal Policy Optimization, which significantly improved solution efficiency and quality compared to traditional methods.
The Integrated Process Planning and Scheduling (IPPS) problem combines process route planning and shop scheduling to achieve high efficiency in manufacturing and maximize resource utilization, which is crucial for modern manufacturing systems. Traditional methods using Mixed Integer Linear Programming (MILP) and heuristic algorithms can not well balance solution quality and speed when solving IPPS. In this paper, we propose a novel end-to-end Deep Reinforcement Learning (DRL) method. We model the IPPS problem as a Markov Decision Process (MDP) and employ a Heterogeneous Graph Neural Network (GNN) to capture the complex relationships among operations, machines, and jobs. To optimize the scheduling strategy, we use Proximal Policy Optimization (PPO). Experimental results show that, compared to traditional methods, our approach significantly improves solution efficiency and quality in large-scale IPPS instances, providing superior scheduling strategies for modern intelligent manufacturing systems.