Machine learning assisted Differential Evolution for the Dynamic Resource Constrained Multi-project Scheduling Problem with Static project Schedules
针对大型模块化建造项目中随机到达的多个相似项目，提出一种结合神经网络和差分进化的方法，在保持调度质量的同时大幅缩短计算时间，对运筹学和项目管理研究者有用。
In large modular construction projects, such as shipbuilding, multiple similar projects arrive stochastically. At project arrival, a schedule has to be created, in which future modifications are difficult and/or undesirable. Since all projects use the same set of shared resources, current scheduling decisions influence future scheduling possibilities. To model this problem, we introduce the dynamic resource constrained multi-project scheduling problem with static project schedules. To find schedules, both a greedy approach and simulation-based approach with varying scenarios are introduced. Although the simulation-based approach schedules projects proactively, the computing times are long, even for small instances. Therefore, a method is introduced that learns from schedules obtained in the simulation-based method and uses a neural network to estimate the objective function value. It is shown that this method achieves a significant improvement in objective function value over the greedy algorithm, while only requiring a fraction of the computation time of the simulation-based method.