###### Abstract
[18] optimized the HFSSP using the algorithm of Nawaz, Enscore and Ham (NEH) heuristic method, and subsequently applied the CS (cuckoo search) algorithm to quickly search and improve on the basis of the optimized solution. [PERSON] et al. [19] proposed an effective artificial bee colony optimization algorithm, and solved the optimal scheduling solution by hybrid representation method. [PERSON] et al. [20] used the variable neighborhood algorithm (HVNS) combined with the chemical reaction optimization algorithm and the distribution estimation algorithm to solve the HFSSP. In the meantime, the algorithm was studied and adjusted to locate the potential region, and search for the optimal solution. [PERSON] and [PERSON] [21] applied the vector notation to model HFSSP, and further proposed an improved discrete artificial bee colony (IDABC) algorithm. Meanwhile, the validity of the IDABC algorithm was verified by using the benchmark problems. [PERSON] et al. [22] proposed heuristic algorithms and two metaheuristic techniques based on an artificial immune system for a two-stage assembly hybrid flow shop scheduling problem. [PERSON] and [PERSON] [23] developed a discrete differential evolution (DDE) algorithm with a modified crossover operator to solve larger sized problems. Other algorithms, such as Quantum-inspired immune algorithm (QIA) [24], Artificial immune system (AIS) [25], have also been applied to solve HFSSP. In addition, there are a lot of non-classical methods for solving this kind of large and complex problems. In order to solve the flow shop problem of the minimized makespan, [PERSON] et al. [26] proposed a feed forward back propagation neural network to solve the problem. [PERSON] and [PERSON] [27; 28] used neural-like structures based on the geometric transformations model as a universal approximator to implement principles of training and self-training, and thus provided the repeatability of training results and large and small training samples as a satisfactory solution. [PERSON] et al. [29] investigated the use of these training algorithms as competitive neural network learning tools to minimize makespan in a flow shop. In the studies mentioned above, although the meta-heuristic algorithms were widely used to solve HFSSP, it still has some disadvantages when encountering large problems, such as complex algorithm frameworks, a lack of real-state mapping and incomplete neighborhood structures [30]. In order to solve these disadvantages, some scholars adopted machine learning to study HFSSP for achieving an efficient scheduling. Monte-Carlo tree search (MCTS) algorithms heuristically build an asymmetric partial search tree by applying machine learning, and then search through the potential branches to solve the optimal strategy by continuously traversing [31]. Therefore, [PERSON] et al. [32] first introduced MCTS for solving production-related problems. A MCTS-based algorithm for the multi-objective flexible job shop scheduling problem was presented by [PERSON] et al. [33], and it was improved by incorporating the Variable Neighborhood Descent Algorithm and other techniques, like rapid action value, which can estimate the heuristic and transposition table. [PERSON] et al. [34] used the improved MCTS algorithm to solve the multi-objective flexible shop scheduling problem, and search the minimum completion time by the adaptive value game comparison. [PERSON] and [PERSON] [35] used an MCTS-based algorithm to find good schedules for a re-entrant scheduling problem. Although good efforts have been made in the above studies, and MCTS is a series of two-person zero-sum game decision-making methods, there are still limitations in the performance and learning efficiency when solving shop floor scheduling problems. For minimizing the makespan, the HFSSP scheduling approach using improved SP-MCTS (single-player Monte-Carlo tree search) is proposed in this paper. [PERSON] et al. [36] proposed a new machine learning algorithm, which is named the SP-MCTS algorithm. [PERSON] et al.