This paper presents a solution of a Cellular Manufacturing system (CMS) considering flow (routing) flexibility and machine flexibility in stochastic and dynamic states. In most research carried out in the past, CMS problems have been solved in static production flow and deterministic production demand. However, there are dynamic flow and stochastic demand in a real-world situation. In this paper, we present a non-linear mixed-integer mathematical model for a CMS problem in dynamic and stochastic production environments. Due to the complexity of the above model, it is so difficult to solve such a model by traditional optimization methods in a reasonable computation time. Thus, tabu search (TS) known as a meta-heuristic algorithm is proposed and a special design of this algorithm is carried out solving the above CMS model.Finally, solutions obtained by tabu search method are compared with solutions reported by the Lingo 6 software in order to validate the efficiency of the proposed algorithms.

To cope with fast changing customer requirements, industrial demands and to meet ‎stringent specifications of customers Cellular Manufacturing Systems has become an ‎effective tool in hands of manufacturers. Most of the published literature on cell ‎formation earlier considers only the data available in the route sheets and ignored sub ‎problems associated with cell formation. There is a need to develop an integrated ‎approach for Cellular Manufacturing, which considers all the aspects. Present paper ‎focuses on developing an integrated approach for Cellular Manufacturing. Proposed ‎method considers workload, volume of production and processing times for machine ‎cell and part family formation which was ignored by past researchers. Proposed ‎method has been tested on one of the largest public sectors in Asia and the benefits ‎the can be obtained by implementation are reported‏.‏

Cellular Manufacturing system (CMS) is highly important in modern Manufacturing methods. Given the ever increasing market competition in terms of time and cost of Manufacturing, we need models to decrease the cost and time of Manufacturing. In this study, CMS is considered in condition of dynamic demand in each period. The model is developed for facing dynamic demand that increases the cost of material flow. This model generates the cells and location facilities at the same time and it can move the machine (s) from one cell to another cell and can generate the new cells for each period. Cell formation is NP-Complete and when this problem is considered in dynamic condition, surly, it is strongly NP- Complete. In this study, genetic algorithm (GA) is used as a meta-heuristic algorithm for solving problems and evaluating the proposed algorithm, Branch and Bound (B & B) is used as a deterministic method for solving problems. Ultimately, the time and final solution of both algorithms are compared.

Most production environments face random, unexpected events such as machine failure, uncertain processing times, the arrival of new jobs, and cancellation of jobs. For the reduction of the undesirable side effects of an unexpected disruption, the initial schedule needs to be reformed partially or entirely. In this paper, a mathematical model is presented to address the integrated cell formation and Cellular rescheduling problems in a Cellular Manufacturing system. As a reactive model, the model is developed to handle the arrival of a new job as a disturbance to the system. Based on the principle of resistance to change, the reactive model seeks a new solution with the minimum difference from the initial solution. This is realized through a simultaneous minimization of the total completion time and the number of displaced machines. For the investigation of the performance of the proposed model, some numerical examples are solved using the GAMS software. The results demonstrate the ability of the reactive model to obtain solutions resistant to unexpected changes