Solving Integrated Process Planning and Scheduling Problem via Graph Neural Network Based Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2409.00968v1
• Date: Mon, 2 Sep 2024 06:18:30 GMT
• Title: Solving Integrated Process Planning and Scheduling Problem via Graph Neural Network Based Deep Reinforcement Learning
• Authors: Hongpei Li, Han Zhang, Ziyan He, Yunkai Jia, Bo Jiang, Xiang Huang, Dongdong Ge,
• Abstract summary: In this paper, we propose a novel end-to-end Deep Reinforcement Learning (DRL) method for IPPS problem. 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. Experimental results show that, compared to traditional methods, our approach significantly improves solution efficiency and quality in large-scale IPPS instances.
• Score: 8.497746222687983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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