基于深度强化学习的随机资源受限多项目动态调度策略.

There are few studies on the problem of stochastic resource-constrained distributed multi-project scheduling (SDRCMPSP） and most of them are static scheduling schemes, which cannot adjust and optimize the strategy in real time according to changes in the environment and respond to frequent dynamic factors in a timely manner. Therefore, this paper established a stochastic resource-constrained multi-project dynamic scheduling DRL model with the goal of minimizing the total drag cost, design the corresponding agent interaction environment, and use the DDDQN algorithm in reinforcement learning to solve the model. The experiment first analyzes the hyperparameters of the algorithm, and then trains and tests the model under two different conditions of variable activity duration and uncertain arrival time, and the results show that the deep reinforcement learning algorithm can obtain scheduling results that are better than any single rule, effectively reduce the total drag-off cost of random resources limited multi-project expectations, and provide a good basis for multi-project scheduling decision optimization. [ABSTRACT FROM AUTHOR]