Abstract

Due to increasing international competition, shorter product life-cycles, variable demand, diverse customer needs and customized products, manufacturers are forced from mass production to the production of large product mixes. In order to adapt to such changes, firms are required to make their manufacturing systems efficient and flexible. Traditional manufacturing systems, such as job shops and flow lines, cannot provide the required efficiently coupled with flexibility to handle these changes. Cellular manufacturing (CM), which incorporates the flexibility of job shops and the high production rate of flow lines, has emerged as a promising alternative. Although CM provides great benefits, its design process is complex for real-life problems. The design process should pass through a number of steps involving several structural and operational aspects. The first important critical step is the formation of part families and machine cells. The effectiveness of this design step heavily depends on the proper consideration of several relevant factors. To this end, a model that incorporates various pragmatic issues is essential. This research is aimed at the development of comprehensive mathematical models to serve in the design of cellular manufacturing systems for dynamic production requirements. In this work, two different mathematical models have been proposed and efficient solution procedures are developed to solve these models.The first mathematical model addresses the design of a dynamic cellular manufacturing system. In this model, the product mix is assumed to vary from period to period where the production quantity of each product during each period is a given data. System reconfiguration is considered to respond to the changing product mix variation. In addition to dynamic system reconfiguration, the model incorporates several pragmatic issues such as alternative routings, lot splitting, sequence of operations, multiple units of identical machines, machine capacity, workload balancing among cells, operation cost, cost of subcontracting part processing, tool consumption cost, setup cost and other practical constraints.The second mathematical model addresses an integrated approach to the design of dynamic cellular manufacturing systems and production planning in MRP environment. The major difference of second model from first one is that in the second model the production lot size of each product during each period is a decision variable but not a given data. The cell formation model is formulated to account for either philosophy allowing the model user to select his/her preference. In order to solve this integrated cell formation and production planning model, two search heuristics, one based on genetic algorithm and the other based on simulated annealing, have been developed.