## 2 Literature Review  
A large number of studies can be found in the literature which solve the problem of determining the batch size and schedule in a HFS. According to [PERSON] et al. [3], a HFS actually represents the production systems where a larger range of products move unidirectional through several production stages. In each production stage, there are one or more identical production capacities. A literature review on hybrid flow shop scheduling problem was given by [PERSON] and [PERSON] [4] and [PERSON] et al. [5]. This type of manufacturing system is characteristic of many processes and discrete manufacturing companies from the real environment [6], such as automobile manufacturing, machinery manufacturing, etc. For this reason, this type of manufacturing system was chosen as the subject of research. A HFS scheduling problem is a complex combinatorial problem. There are many different variations of this problem, i.e., it differs depending on several factors, such as the understanding of the environmental assumptions, constraints or performance measures that are to be achieved [7]. For example, many authors have used different methods to minimize the makespan [8; 9; 10; 11]. Makespan is one of the most frequently used performance measures because makespan directly affects the performance of the manufacturing efficiency in the form of there being satisfying delivery that is on time and reducing the production costs. Additionally, other performance measures have been observed, as can be seen detailed in [12; 13]. Market uncertainty and a change in the production philosophies, where the stocks represent direct losses for many processes and discrete manufacturing companies, necessitate the production of a wide range of products within the timeframe. This leads to an increasing challenge in terms of determining the batch size and schedule. With this realization, the research attention focused on making both decisions at the same time has increased [14]. In addition, there is an increasing number of papers that deal with these problems using real-world examples [15; 16; 17]. Besides batch sizing and scheduling, the problem of accumulating product units between the manufacturing stages is also a major problem. Therefore, it is important to know what capacity of buffer storage to provide for the smooth running of the production. [PERSON] [18] spoke about the influence of a limited buffer in 1990. Makespan minimization problems for a two-stage flow shop with a limited buffer were considered in [19; 20]. [PERSON] and [PERSON] [21] were investigated an energy-oriented scheduling problem deriving from a hybrid flow shop with limited buffers. A solution for multi-objective permutation flow-shop scheduling problems with limited buffers was presented by [PERSON] et al. They proposed an effective hybrid algorithm based on differential evolution [22]. For the same type of problem, [PERSON] et al. [23] developed a multi-objective hybrid self-adaptive differential evolution optimization algorithm. [PERSON] et al. [24] developed a metaheuristic algorithm,which was called the discrete fruit fly algorithm, to solve a batch scheduling problem in a hybrid flow shop with limited buffers. [PERSON] et al. [25] dealt with capacitated batch sizing and a scheduling problem with parallel machines and shared buffers. Batch sizing and scheduling problems with buffer constraints were also presented by [PERSON] and [PERSON] [26]. For solving the batch sizing and scheduling problems and buffer configuration problems, one of the most commonly used methods and techniques is GA. GA has proven to be a relatively good optimization tool. An example of that is given by [PERSON], [PERSON] et al. [16]. [PERSON] et al. [17] proposed an improved compact genetic algorithm in a hybrid flow shop with a multi-queue buffer. [PERSON] et al.