Deep reinforcement learning for solving resource constrained project scheduling problems with resource disruptions |
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| Affiliation: | 1. Department of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha 410004, China;2. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China;3. Department of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China;1. Department of Construction and Manufacturing Engineering. University of Oviedo, 33204 Gijón, Spain;2. Brincker Monitoring. Copenhagen, Denmark;1. Smart & Sustainable Manufacturing Systems Laboratory (SMART LAB), Department of Mechanical Engineering, University of Alberta, Edmonton T6G 1H9, AB, Canada;2. School of Intelligent Manufacturing Ecosystem, Xi’an Jiaotong-Liverpool University, Suzhou 215123, PR China;3. School of Business, Jiangnan University, Wuxi 214122, PR China;1. National NC System Engineering Research Center, Huazhong University of Science and Technology, Wuhan 430074, China;2. Digital Manufacturing Equipment National Engineering Research Center, Huazhong University of Science and Technology, Wuhan 430074, China;3. Wuhan Intelligent Equipment Industrial Institute Co Ltd, Wuhan 430074, China;4. Wuhan Digital Design and Manufacturing Innovation Center Co., Ltd, Wuhan 430074, China |
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| Abstract: | The resource-constrained project scheduling problem (RCPSP) is encountered in many fields, including manufacturing, supply chain, and construction. Nowadays, with the rapidly changing external environment and the emergence of new models such as smart manufacturing, it is more and more necessary to study RCPSP considering resource disruptions. A framework based on reinforcement learning (RL) and graph neural network (GNN) is proposed to solve RCPSP and further solve the RCPSP with resource disruptions (RCPSP-RD) on this basis. The scheduling process is formulated as sequential decision-making problems. Based on that, Markov decision process (MDP) models are developed for RL to learn scheduling policies. A GNN-based structure is proposed to extract features from problems and map them to action probability distributions by policy network. To optimize the scheduling policy, proximal policy optimization (PPO) is applied to train the model end-to-end. Computational results on benchmark instances show that the RL-GNN algorithm achieves competitive performance compared with some widely used methods. |
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