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.

Machines are a key element in the production system and their failure causes irreparable effects in terms of cost and time. In this paper, a new multi-objective mathematical model for Dynamic cellular manufacturing system (DCMS) is provided with consideration of machine reliability and alternative process routes. In this Dynamic model, we attempt to resolve the problem of integrated family (part/machine cell) formation as well as the operators’ assignment to the cells. The first objective minimizes the costs associated with the DCMS. The second objective optimizes the labor utilization and, finally, a minimum value of the variance of workload between different cells is obtained by the third objective function. Due to the NP-hard nature of the cellular manufacturing problem, the problem is initially validated by the GAMS software in smallsized problems, and then the model is solved by two well-known meta-heuristic methods including non-dominated sorting genetic algorithm and multi-objective particle swarm optimization in large-scaled problems. Finally, the results of the two algorithms are compared with respect to five different comparison metrics.

This paper develops a robust optimization approach for a Dynamic cellular manufacturing system (DCMS) integrated with production planning under uncertainty of parts processing time. To deal with this uncertainty, a robust optimization as a tractable approach is adopted. The model includes cell formation, inter-cell layout and production planning concepts under a Dynamic environment. The aim of the model is to minimize inter and intra-cell material handling, inventory holding, back order and reconfiguration costs. To verify the behavior of the presented model and the performance of the developed approach, anumerical example solved in finding an optimal solution.

To design a group layout of a cellular manufacturing system (CMS) in a Dynamic environment, a multi-objective mixed-integer non-linear programming model is developed. The model integrates cell formation, group layout and production planning (PP) as three interrelated decisions involved in the design of a CMS. This paper provides an extensive coverage of important manufacturing features used in the design of CMSs and enhances the flexibility of an existing model in handling the fluctuations of part demands more economically by adding machine depot and PP decisions. Two conflicting objectives to be minimized are the total costs and the imbalance of workload among cells. As the considered objectives in this model are in conflict with each other, an archived multi-objective simulated annealing (AMOSA) algorithm is designed to find Pareto-optimal solutions. Matrix-based solution representation, a heuristic procedure generating an initial and feasible solution and efficient mutation operatorsare the advantages of the designed AMOSA. To demonstrate the efficiency of the proposed algorithm, the performance of AMOSA is compared with an exact algorithm (i.e., Î-constraint method) solved by the GAMS software and a well-known evolutionary algorithm, namely NSGAII for some randomly generated problems based on some comparison metrics. The obtained results show that the designed AMOSA can obtain satisfactory solutions for the multi-objective model.

In order to implement the cellular manufacturing system in practice, some essential factors should be taken into account. In this paper, a new mathematical model for cellular manufacturing system considering different production factors including alternative process routings and machine reliability with stochastic arrival and service times in a Dynamic environment is proposed. Also because of the complexity of the given problem, a Benders’ decomposition approach is applied to solve the problem efficiently. In order to verify the performance of proposed approach, some numerical examples are generated randomly in hypothetical limits and solved by the proposed solution approach. The comparison of the implemented solution algorithm with the conventional mixed integer linear and mixed integer non linear models verifies the efficiency of Benders’ decomposition approach especially in terms of computational time.

This paper presents a multi-objective mixed-integer nonlinear programming model to design a group layout of a cellular manufacturing system in a Dynamic environment, in which the number of cells to be formed is variable.Cell formation (CF) and group layout (GL) are concurrently made in a Dynamic environment by the integrated model, which incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. Additionally, there are some features that make the presented model different from the previous studies.These features include the following: (1) the variable number of cells, (2) the integrated CF and GL decisions in a Dynamic environment by a multi-objective mathematical model, and (3) two conflicting objectives that minimize the total costs (i.e., costs of intra and inter-cell material handling, machine relocation, purchasing new machines, machine overhead, machine processing, and forming cells) and minimize the imbalance of workload among cells.Furthermore, the presented model considers some limitations, such as machine capability, machine capacity, part demands satisfaction, cell size, material flow conservation, and location assignment. Four numerical examples are solved by the GAMS software to illustrate the promising results obtained by the incorporated features.