Reinforcement learning-based algorithm for multi-skill project scheduling problem

Combinatorial explosion is a common phenomenon in multi-skill project scheduling, which leads to higher complexity in multi-skill project scheduling problem (MSPSP) than in traditional single-skill project scheduling problem. Heuristics and meta-heuristics have disadvantages in solving MSPSP. Therefore, based on the characteristics of project scheduling and the algorithmic logic of reinforcement learning, a multi-skilled project scheduling algorithm based on reinforcement learning is designed in this paper. Firstly, the multi-skill project scheduling process is modeled as a Markov decision process (MDP). Then, a double-agent mechanism is proposed, and state integration method and action decomposition method are designed to reduce the complexity of value function learning. Finally, skills conflation algorithm is developed to reduce the time complexity of allocating resources in MSPSP. Comparative experiments between the proposed RL algorithm and heuristics show that the reinforcement learning (RL) has better performance, and experiments between the proposed RL algorithm and meta-heuristics show that the RL has higher stability and shorter running time. © 2024 South China University of Technology. All rights reserved.