This paper presents a new nonlinear mathematical model to solve a Cell Formation Problem which assumes that processing time and interarrival time of parts are random variables. In this research, Cells are defined as a queue system which will be optimized via queuing theory. In this queue system, each machine is assumed as a server and each part as a customer. The grouping of machines and parts are optimized based on the mean waiting time. For solving exactly, the proposed model is linearized. Since the Cell Formation Problem is NP-Hard, two algorithms based on genetic and modified particle swarm optimization (MPSO) algorithms are developed to solve the Problem. For generating of initial solutions in these algorithms, a new heuristic method is developed, which always creates feasible solutions. Also, full factorial and Taguchi methods are used to set the crucial parameters in the solutions procedures. Numerical experiments are used to evaluate the performance of the proposed algorithms. The results of the study show that the proposed algorithms are capable of generating better quality solutions in much less time. Finally, a statistical method is used which confirmed that the MPSO algorithm generates higher quality solutions in comparison with the genetic algorithm (GA).

In this paper a new nonlinear integer programming model is presented for dynamic Cell Formation Problem (DCF) in Cellular manufacturing system (CMS). idea.higher material flow in shorter distance. for Formation of Cells is introduced. Since the linearized model is NP-hard, a novel approach called local search embeded branch and cut (LSEBC) is introduced. Computational results show the efficiency of the LSEBC in comparison with standard comparable methods.

To enhance agility and quick responding to customers' demand, manufacturing processes are rearrenged according to different systems. The efficient execution of a manufacturing system depends on various factors. Among them, Cell design and human issue are the pivotal ones. The proposed model designs Cellular manufacturing systems using three objective functions from three different perspectives, to reflect a more realistic picture of the Cell Formation Problem. This paper presents a model with the goals of maximizing the total value of grouping efficacy and minimizing the total costs and total non-interest workers in Cells in a dynamic environment for several consecutive periods. The main idea of the proposed model is enhancing Cell efficiency through an assigning the group of workers who have a mutual interest in working with each other. For solving the current model, the revised multi-choice goal programming method has been employed. Finally, computational results and sensitivity analysis are discussed.

This paper deals with the Cellular manufacturing system (CMS) that is based on group technology concepts. CMS is defined as identifying the similar parts that are processed on the same machines and then grouping them as a Cell. The most proposed models for solving CMS are focused on Cell Formation Problem while machine layout is considered in few papers. This paper addresses a mathematical model for the joint Problem of the Cell Formation Problem and the machine layout. The objective is to minimize the total cost of inter-Cell and intra-Cell (forward and backward) movements and the investment cost of machines. This model has also considered the minimum utilization level of each Cell to achieve the higher performance of Cell utilization. Two examples from the literature are solved by the LINGO Software to validate and verify the proposed model.

This paper presents a new mathematical model to solve Cell Formation Problem in Cellular manufacturing systems, where inter-arrival time, processing time, and machine breakdown time are probabilistic. The objective function maximizes the number of operations of each part with more arrival rate within one Cell. Because a queue behind each machine; queuing theory is used to formulate the model. To solve the model, two metaheurstic algorithms such as modified particle swarm optimization and genetic algorithm are proposed. For the generation of initial solutions in these algorithms, a new heuristic method is developed, which always creates feasible solutions. Both metaheurstic algorithms are compared against global solutions obtained from Lingo software’s branch and bound (B&B). Also, a statistical method will be used for comparison of solutions of two metaheurstic algorithms. The results of numerical examples indicate that considering the machine breakdown has significant effect on block structures of machine-part matrixes.

Cellular Manufacturing System, is an effective system for economical production of forming of parts. The identification of part families and machine Cells are the main steps of the process. Many approaches have been developed for this Problem in the literature. This Problem, from computational point of view is NP-Comlete. Hence, the application of optimization methods fails to solve relatively large size Problems. In this paper, attempt has been made to apply Simulated Annealing (SA) which is an efficient tool for combinatorial Problems to solve Cellular Manufacturing Systems. Demand for each part due to its inherent uncertainty Volume of interCell moves and minimizing total within Cell load variation has been taken into account. Analysis on the controlling parameters of SA has been performed and the reasonable results have been provided.  

Cell Formation is the first and most important Problem in designing Cellular manufacturing systems. We have modeled the dynamic Cell Formation Problem with respect to the minimization of the interCellular movement and Cellular reconfiguration costs. Due to the nonpolynomiality of the Cell Formation models, a newly introduced metaheuristic namely the grenade explosion method (GEM) is applied to solve the proposed model. We have introduced some modifications to improve the performance of the standard GEM which are modifying the location of the grenade explosion in each step, modifying the correction process of the infeasible solutions and introducing free grenade as a new component of the GEM. The proposed modified GEM is compared to the standard GEM and a simulated annealing algorithm through some numerical examples. The computational results illustrate the preference of the GEM algorithms over the simulated annealing algorithm. Furthermore, the results obtained using the modified GEM are superior to those ones achieved using the standard GEM.

In this paper, a mathematical model is proposed to solve Cell Formation Problem considering alternative process routings in which more than one process route for each part can be selected. The model attempts to minimize interCellular movements and incorporates several real-life production factors and practical constraints. In order to increase the flexibility provided by the multiplicity of routings, the model distributes production volume of each part among alternative routes. Also, a constraint enforcing work load balancing among machines is included in the model. Due to the complexity and combinatorial nature of this model, an enhanced algorithm comprised of a genetic algorithm and a linear programming is proposed for solving the model. The proposed algorithm is tested by a range of test Problems and compared with two algorithms from the literature. The computational results show that the proposed algorithm is effective and the proposed approach offers better solution.