## 2 Literature Review
### Heuristics Approaches  
* Apart from the mathematical programming approaches to resolve Cell Formation problems, most methods are founded on the heuristics which are accumulated into array-based clustering. Other heuristics, which are the combination of multiple techniques, have also been developed by several researchers, including [26; 27]. The drawbacks associated with heuristic approaches are as follows:  
* Machine operations arrangements are ignored. * Numerous visits for the product of the same machine are not considered. * Volumes of the intercellular moves are not considered. * Material handling cost is ignored. * Multiple visits to the same machines are disregarded. * Volume of production, material handling cost, and component cost are not considered. To deal with these shortcomings, researchers developed the GA and Hybrid GA approaches to solve the CMS problems very effectively. GA and Hybrid GA Approaches to Solve Cell Formation and Machine Layout  
The cell arrangement concept was introduced by [PERSON] and [PERSON] [28] via GA and established for group encoding, as opposed to conventional machine encoding, in order to simultaneously create manufacturing cells and select CM groupings. [PERSON] et al. designed a hierarchical GA with intrinsic features, such as a hierarchical chromosomal structure, in order to encode two crucial cell design decisions. These features include (i) a new selection scheme that dynamically takes into account two correlated fitness functions and (ii) a group mutation operator to increase the probability of a mutation occurring. The application of this method yielded results that demonstrate its effectiveness in improving solution quality and accelerating convergence. [PERSON] and [PERSON] [29] proposed utilizing GA to design dependable machine cells and configurations for the production of dynamic parts. The objective was to develop a production cost model that accounts for various manufacturing costs, including production volume, multiple process routes, machine capacity, material handling, and subcontracting of component operation. [PERSON] and [PERSON] [30] utilized a two-layer GA to find a design for dependable CMs that can assist in achieving near-perfect solutions with the lowest potential transportation costs. Using computer tests, the usefulness of the proposed algorithm was demonstrated. Tabriz, Iran's Iranian Diesel Engine Manufacturing Company presented the results of a design case study for a CM. [PERSON] et al. [26] developed an electromagnetism-like (EM-like) method and a Genetic Algorithm to solve the design problem of inter- and intracellular architecture in a dynamic environment. The goal was to reduce the overall cost of material handling flows during cell rearrangement and intra- and inter-cell. In order to evaluate the performance of this pairing of two algorithms, statistical tests were conducted on a number of numerical examples divided into small, medium, and large problems that were solved using the proposed method. The test results demonstrated that the proposed strategy outperformed the competing algorithms. The primary objective of [PERSON] et al. [31] was to discover how to solve the issues of Cell Formation and layout design. Changes to the GA parameter set resulting from research have an effect on the CMS issue. Since noise factors are represented by the size of the matrix in that study, they used the Taguchi method to determine the optimal combination of GA parameters that can increase the algorithm's efficiency and to determine whether this optimal combination is influenced by noise factors. The objectives of the proposed GA in [PERSON]'s study [32] include simultaneously designing the input and output locations for each cell's stations and figuring out the material handling systems' flow paths. The total material handling cost for the GA that was used to optimize the solution design was the lowest. The suggested approach was put to the test on four different layouts using various sets of the original problem data.