The resource-constrained project scheduling problem (RCPSP) is one of the project scheduling problems which are widely used in construction and many industrial disciplines. The challenge of the problem is to design some appropriate search mechanism for finding solutions in feasible space. An improved genetic algorithm based on time window decomposition is proposed in this paper. Three derivation methods are applied to increase population diversity. The sampling count allocation strategy and the use of destructive lower bounds improve the search efficiency. The computational experiments on PSPLIB show that the proposed approach is more effective than that only using the decomposition mechanism and is competitive in solving two real-life cases. This research illustrates that continuously changing the search subspaces has potential advantages, which may be useful for studying RCPSP using other evolutionary algorithms in future. Some other better results may be obtained by using machine learning methods to flexibly determine the sampling times for each individual.

资源约束项目调度问题（RCPSP）是工程调度问题中的一种，广泛应用于建筑和许多工业学科。该问题的挑战在于设计一些适当的搜索机制，以便在可行空间中找到解决方案。本文提出了一种基于时间窗分解的改进遗传算法。应用三种推导方法来增加种群多样性。采样计数分配策略和破坏性下限的使用提高了搜索效率。在 PSPLIB 上的计算实验表明，所提出的方法比仅使用分解机制的方法更有效，并且在解决两个实际案例方面具有竞争力。这项研究表明，不断改变搜索子空间具有潜在的优势，这可能对将来使用其他进化算法研究 RCPSP 很有用。通过使用机器学习方法灵活确定每个个体的采样时间，可能会获得其他一些更好的结果。