The hybrid flow shop scheduling (HFS) model has significant practical applicability in fields, such as manufacturing, transportation, service, and communication. However, learning effects, refer to the phenomenon of processors spending less processing time with more familiar operations, which are common constraints in actual production while often ignored in HFS research despite their significant impact on processing efficiency. In this article, an HFS problem with learning effects and release dates is investigated from a practical application perspective. For large-scale instances, a dispatching rule-based heuristic is developed with theoretical performance guarantees by demonstrating the asymptotic optimality and the tight worst-case bound. For small-scale instances, a branch-and-boun3d algorithm is designed to obtain an exact solution. An elaborate branching scheme, an idle-time-based pruning rule, and a task-splitting-based lower bound effectively reduce the search space. For medium-scale instances, a hybrid shuffled frog-leaping algorithm combined with macro evolution and local intensification is presented to search for high-quality solutions. Extensive experiments demonstrate the superiority of the developed algorithms against the state-of-the-art algorithms.