**2. Cellular manufacturing philosophy**

In recent trends, a prominent example of GT implementation is the cellular manufacturing philosophy. The similarity in design and/or manufacturing attributes among parts promotes standardization and common processing. The specialty of CMS is to divide the available machines into some machine cells [1]. CMS increases the flexibility and variety in production. This leads to limiting the drawbacks of job shop and flow type manufacturing philosophies. CMS offers main advantages like significant improvement in material flow, reduction in material storage, reduction in material handling. These advantages give reduced cumulative lead time as an outcome [1].

#### **2.1 CMS approaches**

Generally, there are two types of CMS approaches for the creation of machine cells and part families. (1) Simple CMS approach: This is used to alter part machine matrices and (2) The sophisticated CMS approach: This one is used to handle constraints like maximum cell numbers, varying demand for products and setup. Under each of these two, there are again two types of CMS according to part population. (1) CMS with static part population: The market demand and part

population remain constant over a certain period of time in this type of CMS. This CMS is best suited for a single level planning horizon. (2) CMS with dynamic part population: The market-based environment is considered in this type of CMS. Particularly, varying demand of product in market and the machine breakdown have an effect on the performance of a CMS design from one period to other.

In real world manufacturing cases, design of CMS becomes difficult and essentially requires allotment of resources in various machine cells and creation of part families. Therefore, a robust CMS design is necessary to be obtained to manage changes in size of the product demand, processing times, sequence of operations, material handling, plant layout and maintenance of machines without affecting the continuity of production [1].

#### **2.2 A robust CMS design approach**

In a particular cellular manufacturing environment, a robust model for constant production can be designed. Each part has multiple process plans and alternative process routes. Design of robust machine cell is considered where static part population is assumed. A part goes through the sequence of multiple manufacturing operations. The sequential operations are carried out in different machine cells. They include similar machines having more than one scope of operations and restricted potential for processing part families [1].

 Before clustering the machine cell, it is essential to determine the process route which is optimal rather than adopting multiple process routes predefined by users. At the starting of planning phase, this model can arrange machines in manufacturing cells and the optimum production plan can be determined after that. The motive of a robust CMS design approach is to optimize overall cost. Overall cost is affected by different costs: [1]. machine attainment cost (C1), machine working cost (C2), production cost for part operation (C3), intercellular material handling cost (C4), intracellular material handling cost (C5) and subcontracting cost for part operation (C6).

A robust CMS design has the following constraints [1]. Each machine has a part operation assigned to it. The part demand is satisfied in a predefined time frame. Inner part operation is limited to the capacity and availability of machines. The material flow conservation is taken into consideration. The number of machine types is less than or equal to the total number of the same machines. The cell size ranges between upper and lower cell limits. Logical binary and positive integers are essential for the formulation of equations with restrictions and constraints (**Figure 2**).

#### **2.3 Robust optimization model (ROM)**

This model was introduced in 1995 by Mulvey for handling the trade-off associated with the anticipated cost and its varying randomly determined programs [2].

Robustness has two types: solution robustness in which solution is near to optimum total scenarios and model robustness in which solution is near to feasible in total scenarios [2].

The main objective of robustness is "to find a robust solution which ensures that any realization of the scenario is almost the optimum in response to changing input data. Solution of model robustness should be robust with respect to feasibility if it remains "almost" feasible for all the scenarios" [2]. The goal of the proposed model is to optimize total cost of holding, material handling, external transportation and fixed costs for carrying out manufacturing operations for each part in each plant, machine attainment cost and to meet uncertainty demands in various cases [2].

*An Optimization Model for a Manufacturing Plant Using Cellular Manufacturing: A Review DOI: http://dx.doi.org/10.5772/intechopen.81083* 

**Figure 2.**  *A robust machine cell design flow-chart.* 
