Showing 6 results for Cell Formation
I. Mahdavi, M. M. Paydar, M. Solimanpur , M. Saidi-Mehrabad,
Volume 21, Issue 2 (5-2010)
Abstract
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.
Parviz Fattahi, Bahman Ismailnezhad,
Volume 27, Issue 2 (6-2016)
Abstract
In this paper, a stochastic cell formation problem is studied using queuing theory framework and considering reliability. Since 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 initial solutions in these algorithms, a new heuristic method is developed, which always creates feasible solutions. Moreover, full factorial and Taguchi methods are implemented to set crucial parameters in the solutions procedures. Deterministic method of branch and bound (B&B) algorithm is used to evaluate the results of modified particle swarm optimization algorithm and the genetic algorithm. The results indicate that proposed algorithms have better performance in quality of the metaheurstic algorithms final answer and solving time compared with the method of Lingo software’s B&B algorithm. The solution of two metaheurstic algorithms is compared by t test. Ultimately, the results of numerical examples indicate that considering reliability has significant effect on block structures of machine-part matrixes.
Bahman Esmailnezhad, Mohammad Saidi-Mehrabad,
Volume 29, Issue 1 (3-2018)
Abstract
This paper deals the stochastic cell formation problem (SCFP). The paper presents a new nonlinear integer programming model for the SCFP in which the effect of buffer size on the grouping efficacy of cells has been investigated. The objective function is the maximization of the grouping efficacy of cells. A chance constraint is applied to explore the effect of buffer on the SCFP. Processing time and arrival time of the part for each cell are considered stochastic and are following exponential probability distribution. To find out the optimal solution in a reasonable time, a heuristic approach is used to linearize the proposed nonlinear model. This problem has been known as an NP-hard problem. Therefore, two metaheuristic methods, namely; genetic algorithm and particle swarm optimization are employed to solve examples. The parameters of the algorithms are calibrated using Taguchi and full factorial methods, and the performances of the algorithms on the examples of various sizes are analyzed against global solutions obtained from Lingo software’s branch and bound (B&B) in terms of quality of solutions and computational time.
Amir-Mohammad Golmohammadi, Mahboobeh Honarvar, Hasan Hosseini-Nasab, Reza Tavakkoli-Moghaddam,
Volume 29, Issue 2 (6-2018)
Abstract
The fundamental function of a cellular manufacturing system (CMS) is based on definition and recognition of a type of similarity among parts that should be produced in a planning period. Cell formation (CF) and cell layout design are two important steps in implementation of the CMS. This paper represents a new nonlinear mathematical programming model for dynamic cell formation that employs the rectilinear distance notion to determine the layout in the continuous space. In the proposed model, machines are considered unreliable with a stochastic time between failures. The objective function calculates the costs of inter and intra-cell movements of parts and the cost due to the existence of exceptional elements (EEs), cell reconfigurations and machine breakdowns. Due to the problem complexity, the presented mathematical model is categorized in NP-hardness; thus, a genetic algorithm (GA) is used for solving this problem. Several crossover and mutation strategies are adjusted for GA and parameters are calibrated based on Taguchi experimental design method. The great efficiency of the proposed GA is then demonstrated via comparing with particle swarm optimization (PSO) and the optimum solution via GAMS considering several small/medium and large-sized problems.
Amir-Mohammad Golmohammadi, Mahboobeh Honarvar, Guangdong Guangdong, Hasan Hosseini-Nasab,
Volume 30, Issue 4 (12-2019)
Abstract
There is still a great deal of attention in cellular manufacturing systems and proposing capable metaheuristics to better solve these complicated optimization models. In this study, machines are considered unreliable that life span of them follows a Weibull distribution. The intra and inter-cell movements for both parts and machines are determined using batch sizes for transferring parts are related to the distance traveled through a rectilinear distance. The objectives minimize the total cost of parts relocations and maximize the processing routes reliability due to alternative process routing. To solve the proposed problem, Genetic Algorithm (GA) and two recent nature-inspired algorithms including Keshtel Algorithm (KA) and Red Deer Algorithm (RDA) are employed. In addition, the main innovation of this paper is to propose a novel hybrid metaheuristic algorithm based on the benefits of aforementioned algorithms. Some numerical instances are defined and solved by the proposed algorithms and also validated by the outputs of exact solver. A real case study is also utilized to validate the proposed solution and modeling algorithms. The results indicate that the proposed hybrid algorithm is more appropriate than the exact solver and outperforms the performance of individual ones.
Saeed Dehnavi, Ahmad Sadegheih,
Volume 31, Issue 1 (3-2020)
Abstract
In this paper, an integrated mathematical model of the dynamic cell formation and production planning, considering the pricing and advertising decision is proposed. This paper puts emphasis on the effect of demand aspects (e.g., pricing and advertising decisions) along with the supply aspects (e.g., reconfiguration, inventory, backorder and outsourcing decisions) in developed model. Due to imprecise and fuzzy nature of input data such as unit costs, capacities and processing times in practice, a fuzzy multi-objective programming model is proposed to determine the optimal demand and supply variables simultaneously. For this purpose, a fuzzy goal programming method is used to solve the equivalent defuzzified multi-objective model. The objective functions are to maximize the total profit for firm and maximize the utilization rate of machine capacity. The proposed model and solution method is verified by a numerical example.
