In order to obtain a rapid respond to market demands, the concept of Group Technology was introduced in mid 20 th century which is defined as a manufacturing philosophy and strategy which groups parts in specific families due to their similarities from different aspects such as operation needed, process route, design and etc. and constructs a manufacturing cell for each or a family of parts. This kind of manufacturing is called cellular manufacturing. An important issue in the field of cellular manufacturing is the issue of human resource, since eventually humans will set up and organize the cell and the efficiency of the cell is remarkably depended on utilizing efficient human resources. From human resource point of view, cells are divided to two groups of manned and unmanned where manned cells are also divided to sets of labor intensive and machine intensive. In machine intensive cells, due to automated machines, manufacturing operations are performed automatically. As a result, while machining operations in a station, the operator will have the opportunity to meet the needs of other operating stations. This case, moving from one station to another and perform some manual operations simultaneously in different stations, is called labor flexibility. Utilizing flexible work force leads to a decrease in the number of operators needed, higher workload balancing, and higher efficacy of the cell. An appropriate assignment of operators to in-cell tasks considering workforce flexibility can result in maximum efficiency of the cell in addition to utilizing human resources in an efficient manner. In this dissertation, in order to assign workforce in cellular manufacturing considering labor flexibility, a model is proposed where a feasible assignment strategy is initially introduced on its basis. Any assignment strategy consists of three input parameters: the number of operators, workload sharing, and transfer batch size. In these strategies, the value of output parameters is determined via simulation of the cell. Then using Data Envelopment Analysis (DEA), output oriented BCC model, allocation strategies are compared and the most efficient one is selected. In the next step, efficient strategies are ranked using modified MAJ model and the best assignment strategy is determined in such a way that beside determination of an appropriate transfer batch size, best values for output parameters are obtained. At the end, an illustrative example is presented to clarify the applications of the proposed model and then some points are demonstrated based on its computational results which are applicable in various manufacturing cells. Keywords Cellular Manufacturing, Lobar assignment, Lobar Flexibility, Data Envelopment Analysis, Ranking Efficient Decision Making Units