## 2 Literature Review
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Reference** & **Optimization Goal** &
\begin{tabular}{c} **Generative Method** \\ **Category** \\ \end{tabular} & **Generative Method** & **Evalative Method** \\ \hline [PERSON] and [PERSON] [14] & Max throughput & Dynamic programming & Dynamic programming & Aggregation \\ [PERSON] et al. [15] & Max throughput & Dynamic programming & Dynamic programming & Decomposition \\ [PERSON] and [PERSON] [16] & Max throughput & Metaheuristics & Simulated annealing & Decomposition \\ [PERSON] et al. [17] & Max throughput & Metaheuristics & Simulated annealing & Decomposition \\ [PERSON] et al. [18] & Max profit & Metaheuristics & Genetic algorithm & Aggregation \\ [PERSON] et al. [19] & Max profit & Metaheuristics & Genetic algorithm & Aggregation \\ [PERSON] et al. [20] & Max throughput & Metaheuristics & Tabu search & Decomposition \\ [PERSON] [6] & Max throughput & Metaheuristics & Tabu search & GEM \\ [PERSON] et al. [21] & Max throughput & Metaheuristics & Particle swarm & Simulation \\ [PERSON] et al. [22] & Max throughput & Metaheuristics & Artificial immune & Decomposition \\ [PERSON] et al. [23] & Max throughput & Metaheuristics & Degraded ceiling & Decomposition \\ [PERSON] and [PERSON] [24] & Max throughput & Metaheuristics & Big bang–big crunch & Decomposition \\ [PERSON] et al. [25] & Max throughput & Metaheuristics & Variable neighborhood & Decomposition \\ [PERSON] and [PERSON] [26] & Max throughput & Metaheuristics & Hybrid & Simulation \\ [PERSON] and [PERSON] [27] & Max profit & Metaheuristics & Hybrid & Decomposition \\ [PERSON] et al. [28] & Min total capacity & Search & Bottleneck & Simulation \\ [PERSON] and [PERSON] [4] & Max throughput & Search & Gradient & Decomposition \\ [PERSON] and [PERSON] [4] & Min capacity & Search & Gradient & Decomposition \\ [PERSON] and [PERSON] [11] & Max profit & Search & Gradient & Decomposition \\ [PERSON] et al. [29] & Min cost & Search & Gradient & Decomposition \\ [PERSON] [30] & Max profit & Search & Gradient & Decomposition \\ [PERSON] and [PERSON] [12] & Max profit & Search & Gradient & Decomposition \\ [PERSON] et al. [31] & Max throughput & Search & Decomposition & Decomposition \\ [PERSON] et al. [32] & Min WIP & Search & Decomposition & GEM \\ [PERSON] et al. [33] & Min capacity & Search & Decomposition & Decomposition \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the BAP literature. the BAP [14; 15]. However, existing dynamic programming methods have some restrictive assumptions that may limit their applicability, and most of them were developed for reliable production lines. Metaheuristics can be defined as high-level, problem-independent algorithmic frameworks that provide a set of guidelines to enable the development of heuristic optimization algorithms. Various metaheuristics have been applied to BAPs, including simulated annealing [16; 17], genetic algorithm [18; 19], tabu search [6; 20], particle swarm optimization [21], artificial immune [22], degraded ceiling [23], big bang-big crunch [24], and variable neighborhood search [25]. Recently, several authors have suggested hybridizing metaheuristics with other search techniques. [PERSON] et al. [48] proposed a hybrid of the genetic algorithm and branch-and-bound method. In the study by [PERSON] and [PERSON] [26], a hybrid genetic algorithm and simulated annealing approach was proposed for the allocation of buffers in open serial production lines. [PERSON] and [PERSON] [27] put forward a hybrid variable neighborhood search method, incorporating large neighborhood searches as a diversification strategy. Search algorithms obtain near-optimal solutions by searching for neighborhood solutions according to heuristic rules or mathematical principles. [PERSON] et al. [28] developed a bottleneck-based local search algorithm for buffer allocations in flow lines with limited supply. [PERSON] and [PERSON] [4] proposed two efficient gradient algorithms to solve the primal and dual BAPs, respectively.