This paper is in search of designing the Cellular Manufacturing Systems (CMSs) under Dynamic and flexible environment. CM is proper for small-to-medium lot production environment that helps the companies to produce variable kind of productions with at least scraps. The most important benefits of CM are decline in material handling, reduction in work-in-process, reduction in set-up time, increment in flexibility, improved quality, and shorter lead time. In this research A multi-objective mixed integer model is presented that considers some real-world critical conditions same as costs of multi-period cell formation and production planning, human resource assignment to cells and balancing workload of cells. This model groups the parts and machines concurrently with labor assignment This study aims to 1) minimize various costs including reassignment cost of human resource, the batch inter-cell material handling cost, constant and variable cost of machines, relocation and purchase cost of machines, 2) minimize cell load variation and 3) maximize utilization rate of human resource. The model is complicate, so it is verified with Lingo 8.0. Soft ware. Since particle swarm optimization approach less than many other metaheuristic approaches have been applied to solve multi-objective CMS problems so far, we utilize this method to solve our model. The results are presented at the last part.

Companies and firms, nowadays, due to mounting competition and product diversity, seek to apply virtual Cellular Manufacturing Systems to reduce production costs and improve quality of the products. In addition, as a result of rapid advancement of technology and the reduction of product life cycle, production Systems have turned towards Dynamic production environments. Dynamic Cellular Manufacturing environments examine multi-period planning horizon, with changing demands for the periods. A Dynamic virtual Cellular Manufacturing system is a new production approach to help manufacturers for decision making. Here, due to variability of demand rates in different periods, which turns to flow variability, a mathematical model is presented for Dynamic production planning. In this model, we consider virtual cell production conditions and worker flexibility, so that a proper relationship between capital and production parameters (part-machine-worker) is determined by the minimum lost sales of products to customers, a minimal inventory cost, along with a minimal material handling cost. The problems based on the proposed model are solved using LINGO, as well as an epsilon constraint algorithm.

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.

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.

Designing an appropriate industrial clusters model as a general method in the regional economy is an important problem for economic and industrial development planners. Paying attention to the objectives of sustainable development and the green economy, in addition to the traditional objectives of economic growth, is the need of countries today. In order to meet the goals of sustainable development, the multi-objective mathematical programming and Dynamic model of industrial clusters was designed and tested to optimize four objectives: profit, employment, cost of transportation of materials and environmental appraisal score of the cluster. In designing parts of the model in the assignment of firms and the establishment of relations between and within cooperation networks, the concepts and research literature of Cellular Manufacturing were used because of proximity of the concepts and extent of studies in this field. The problem solving results by decomposition methods and weighting functions objectives with experts' opinion and analysis of experiments confirmed that the optimal results of experiments have been presented 99% improvement in minimizing the metric function of the model.

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