**3. Description of CMS study**

Eric Molleman et al. proposed that cellular manufacturing system (CMS) should reflect the interrelated features like cell formation and functionally organized manufacturing units. Analysis of these features offers some factors that are required to be taken into consideration. The data mentioned here depends on document study and asking questions of different level employees. The documents analyzed consist of process planning sheets, performance reports and review cards of important meetings and audits.

 The main merit of cellular manufacturing is its ability to connect all types of sequential operations in the organization. Various manufacturing steps are put together through GT. Moreover, the sequential support functions including process planning and production planning can be more easily functionalized in the case of CM [3]. Logendran et al. found that allocation of parts and machines to every machine cells in CMS results in different types of features consisting grouping of parts into part families that is done by attainment of machines to part families and simultaneous arrangement of parts using GT. A relevant model and an algorithm have been developed and presented for converting an actual FM system into a CM system. All operational conditions and factors related to the analysis have been included in the development of the model [4]. Bazargan-Lari et al. described the use of the latest included model for designing cellular manufacturing, including the parts/machines and their clustering and material layout designs. Combined layout design can be extended to address all stages in the design of machine cells. It was shown how the real cell-formation algorithms can be modified and used to generate various part machine incident matrices. This proposed, combined model provides multiple efficient alternative solutions according to different cell-partition methods. It offers the means to calculate every alternative of major benefits and criteria such as needed area for manufacturing, calculation of material handling, machine criteria, planning horizon etc. The capacity of the proposed method was shown by applying it to a real manufacturing CM case [5]. Selim Akturk reported that the motive of the model is to optimize the altering cost of production subject to manufacturing and inventory balance constraints for part families and different items, capacity feasibility constraints for GT cells and resources over the planning horizon. An average planning model of the cell loading problem

 for CM systems has been developed to optimize the variable production cost. The proposed approach has several merits over models in the current literature on layer by layer planning and cell formation [6]. Shayan et al. reported some of the major results and discussions of the effects of using a cellular manufacturing environment on the production rate of people, management and facilities. The cell formation in the industrial production unit under investigation clearly has positive results on the employee's behavioral patterns such as committed work approach, innovative work patterns that causes better productivity. The performance improvement in turn decreases the production costs and causes quality improvement of the production [7]. Urban Wemmerlöv et al. reported a large number of research topics related to cellular manufacturing, discussed the necessity for their investigation and suggested proper methods for their study [8]. Ghezavati et al. reported a novel mathematical robust approach for a cellular manufacturing problem combined with important factors of supply chain network qualities in the presence of uncertain inner parameters and exterior parameters [9]. Wang et al. prescribed an assignment of machine-cells to linear locations in order to optimize the material handling cost for different cells occurred because of problematic machines in a cellular manufacturing system [10]. Burgess et al. compared a factory designed as a traditional job shop for the identical firm structured as a hybrid firm including cellular manufacturing unit [11]. Javadi et al. presented a comprehensive model for cell formation and layout design in CMS [12]. Cao et al. formulated a mathematical programming technique for optimal lot division into alternative ways to consider for the effect of manufacturing run length on part quality in a CMS environment [13]. Logendran et al. developed and presented a new approach consisting of two important factors for deciding optimal and near-optimal machine-part incident matrix clustering in cellular manufacturing [14]. Madhusudanan Pillai et al. presented a new approach of robust design for creating part families and machine cells that can handle all the variations in demands and product mixes without any relocations [15]. Taboun et al. presented a mixed integer mathematical model for simultaneous machines and parts grouping and assignment when part incident machine mix matrices and demand changes across multiple timeframes according to forecasts and/or product life cycles [16]. Mak et al. presented a method to solve the manufacturing cell formation and the production scheduling problems for designing virtual VCMS [17]. Bazargan-lari et al. developed a new model based on merging pre-emptive goal programming and simulated annealing for machine layout in cells [18]. Mohammad Rezaei-Malek et al. considered decision-making as an operator's personal quality index for designing a psychologically consistent CMS [19]. Vohra et al. presented a non-heuristic network approach to creating manufacturing cells with the least intercellular movements [20].
