## 2 Related Work  
Shop scheduling problems can be classified according to classification criteria such as numbers of machines and resource constraints [16]. They can be classified into job-shop, flow-shop, and open-shop scheduling problems. This study addresses flow-shop scheduling problems because each job proceeds in a certain flow sequence by machines [17; 18]. Hybrid flow-shop scheduling problems are more complex than simple flow-shop scheduling problems. As shown in Figure 3, hybrid flow-shop scheduling is a flow-shop problem with parallel machines, in which several similar or identical machines process in each stage. This study addresses a multi-process line scheduling problem [10; 19; 20]. Several machines can be selected at a stage, and the processing time varies by the process; therefore, selecting the appropriate machine to perform the process every time is necessary. Although scheduling has various objectives, it generally minimizes the total time required to complete all tasks [17; 18; 21; 22]. Typically, shop scheduling is a problem that combines various tasks, necessary processes, and various machines to perform these processes. Previous studies have shown that the problem complexity is very high because the obtained solutions of shop scheduling problems are typically numerous; furthermore, choosing which of these is optimal is a more difficult NP-hard problem [21; 23; 24]. Attempts have been made to solve scheduling problems with many different types of techniques, and approximation methods using heuristic and metaheuristic approaches have been used more frequently than accurate methods. However, they require considerable computational time [11]. Heuristic techniques include dispatching rule methods, such as the first-come-to-a-queue, first-service rule (FCFS), shortest processing time rule (SPT), longest processing time rule (LPT), and smallest remaining processing time rule (SRPT) [11; 22; 25]. Because of the  
Figure 3: Flowchart of hybrid flow-shop scheduling. advantage of simple and easy implementation, scheduling logic using dispatching rules is commonly used. However, in an environment with many exceptions and high complexity, it is hard to obtain high-performance schedules by applying only one basic priority rule. Metaheuristic methods, such as Tabu search and genetic algorithm techniques, are independent of specific problems, and can be applied to various scenarios [11]. A genetic-algorithm-based approach that employed additional local search methods using more than one crossover operator was proposed [3]. Investigations based on the local neighbourhood parallel search method embedded in the Tabu search approach have also been performed [2]. There has also been a study using branch and bound algorithms in the manufacturing of components. This was a study that reflected realistic factors, such as considering buffer restrictions, and did achieve a slight performance improvement over conventional heuristic methods [5]. There has been a genetic-algorithm-based study that generated arbitrary neural networks capable of estimating task sequences [6]. A study has also been conducted to study efficient rule generation using gene expression programming [7]. However, generally, these methods have disadvantages: the difficulty in finding the optimal solution and the computation time increase required to obtain the optimal solution as the complexity of the problem increases. In our study, the location of materials that change in real time and a large environmental space increase the complexity of the problem. Reinforcement learning techniques are being used to solve the above-mentioned flow-shop scheduling problem. There has been a paper that studied the enhanced e-greedy method designed as a trade-off of exploration and exploitation [26]. A study has been conducted using algorithms based on time differences by designating basic priority dispatching rules, such as FCFS, SPT, LPT, and SRPT, as actions and assigning a negative reward if a machine is inoperable [27].