and finished goods inventories, Condensed Throughput time and Improvement in working flexibility. Cellular Manufacturing system consists of a group of machines forming different cells. These cells are specialized in producing a specific part family. Cellular manufacturing has also become popular where different machines are grouped into a cell which is specialized in producing part family. Part family can be defined in the way where some parts have similar shapes and sizes or have similar processes steps. In CM, the issue is development of an efficient cell. Many techniques have been described to cope with this issue. Three approaches are used for cell formation: (a) part- family grouping, in which part families are formed and then machines are grouped into cells; (b) grouping of machines, in which cells of machines are formed taking into account homogeneity in movements and parts are assigned to cells; (c) machine-part grouping, in which part families and machine cells are formed all together. In this paper, objective is minimization of inter-cellular movements of parts and the focus in our case is identification of parts or machines to be grouped into cell. The work has been carried out in Genetic Algorithm (GA), in which several generations are solved to achieve multiple objective function values and best chromosome with minimum objective function is encoded to get optimal solution. Mutation and cross over operators are used in GA and are responsible for next generation. In crossover, information is combined from two parents to new offspring or child. By using crossover operator, best genes are removed from many chromosomes and then recombining them into better offspring. In mutation, new offspring are generated by making random changes in existing generation. It adds diversity to the population and increase the likelihood that best fitness value of the chromosomes can be generated. By using GA both constraint and unconstraint problems can be solved and usually it is applied in complex system where no of machines and production is very high. Technical Journal, University of Engineering and Technology Taxila, Vol. 19 No. II-2014 16 Abstract-Cell formation in cellular manufacturing is necessary to achieve desired productivity, efficiency and quality. Genetic algorithm so developed is applied in cellular manufacturing system to design cells. Cells are arranged according to certain criteria and are defined by objective function which is evaluated for chromosome in each population. Objective function is to minimize intercellular movements because it increase lead time, work in process, cost of material handling and fatigue of workers. Genetic algorithm is biological evolution and natural selection process which is alternatively used for solving complex optimization problems. A population consisting of ten chromosomes has been created randomly and objective function value is calculated for each chromosome. Using genetic operators ten generations have been created and objective function value of each chromosome calculated using MATLAB for each th generation. In 10 generation a chromosome with minimized objective function is achieved and encoded to design cells with minimum intercellular movements of parts. It has been learnt that about fifty percent intercellular movements have been reduced by Genetic algorithm. Keywords-Cellular Manufacturing, Cross Over, Elitism, Genetic Algorithm, Mutation, Population, cell formation I. INTRODUCTION Group technology has wide applications, and it has become popular in shop floor layout design. The operational benefits of flow line production can be achieved by using Cellular Manufacturing (CM) System. A group of parts, which are similar in some properties, are brought on a machine group in cellular manufacturing system to perform various operations. CM provides excellent results in the courses of simulation, analytical, surveys and actual implementations. They are: Reduction of setup time, Condensed Lot sizes, Reduction in Work-in-process Minimization of Intercellular Movements in Cellular Manufacturing System Using Genetic Algorithm 1 2 3 M. Imran , N. Iqbal , M. Jahanzaib 1,2 Industrial Engineering Department UET Taxila, Pakistan 2 Industrial Engineering Department UET Taxila, Pakistan 1 imran.ime13@gmail.com 2 neelumiqbal@yahoo.com 3 jahan.zaib@uettaxila.edu.pk