【Abstract】Problem definition: Intraproject learning in project scheduling involves the use of learning among the similar tasks in a project to improve the overall performance of the project schedule. Under intraproject learning, knowledge gained from completing some tasks in a project is used to execute similar later tasks in the same project more efficiently. We provide the first model and solution algorithms to address this intraproject learning problem. Academic/practical relevance: Intraproject learning is possible when, for example, the difficulty of the tasks becomes better understood, or the efficiency of the resources used becomes better known. Hence, it is necessary to explore the potential of intraproject learning to further improve the performance of project scheduling. Because learning consumes time, firms may underinvest in intraproject learning if they do not recognize its value. Although the project scheduling literature discusses the potential value of using obtained information from learning within the same project, we formally model and optimize the use of intraproject learning in project scheduling. Methodology/results: We model the tradeoff between investing time in learning from completed tasks and achieving reduced durations for subsequent tasks to minimize the total project cost. We show that this problem is intractable. We develop a heuristic that finds near optimal solutions and a strong relaxation that allows some learning from partially completed tasks. Our computational study identifies project characteristics where intraproject learning is most worthwhile. In doing so, it motivates project managers to understand and apply intraproject learning to improve the performance of their projects. A real case is provided by a problem of the Consumer Business Group of Huawei Corporation, for which our model and algorithm provide a greater than 20% improvement in project duration. Managerial implications: We find consistent evidence that projects in general can benefit substantially from intraproject learning, and larger projects benefit more. Our computational studies provide the following insights. First, the benefit from learning varies with the features of the project network, and projects with more complex networks possess greater potential benefit from intraproject learning and deserve more attention to learning opportunities; second, noncritical tasks at an earlier project stage should be learned more extensively; and third, tasks that are more similar (or have more similar processes) to later tasks also deserve more investment in learning. Learning should also be invested more in tasks that have more successors, where knowledge gained can be used repetitively.