Interval-censored failure time data as a general type of survival data often arises in medicine and other applied fields. Survival tree is a flexible predictive method for survival data because no specific assumptions are required.

Generalized Log-Rank Test have good power with parameters for interval-censored failure time data. We construct a special test statistic of Generalized Log-Rank Tests, and propose a new survival tree with hyper-parameter by combining the test statistic with Conditional Inference Framework for interval-censored failure time data. The effect of tuning hyper-parameter are discussed and hyper-parameter tuning allows the tree method to be more general and flexible. Thus the tree method either improve upon or remain competitive with existing tree method for interval-censored failure time data-ICtree, which is a special case of ours. An extensive simulation is executed to assess the predictive performance of our tree methods. Finally, the tree methods are applied to a tooth emergence data.

The lot-streaming flowshop scheduling problem with equal-size sublots (ELFSP) is a significant extension of the classic flowshop scheduling problem, focusing on optimize makespan. In response, an improved dynamic Q-learning (IDQL) algorithm is proposed, utilizing makespan as feedback. To prevent blind search, a dynamic $\epsilon$-greedy search strategy is introduced. Additionally, the Nawaz-Enscore-Ham (NEH) algorithm is employed to diversify solution sets, enhancing local optimality. Addressing the limitations of the dynamic $\epsilon$-greedy strategy, the Glover operator complements local search efforts. Simulation experiments, comparing the IDQL algorithm with other intelligent algorithms, validate its effectiveness. The performance of the IDQL algorithm surpasses that of its counterparts, as evidenced by the experimental analysis. Overall, the proposed approach offers a promising solution to the complex ELFSP, showcasing its capability to efficiently minimize makespan and optimize scheduling processes in flowshop environments with equal-size sublots.