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Showing 4 results for Cell Formation Problem

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.


Bardia Behnia, Iraj Mahdavi, Babak Shirazi, Mohammad Mahdi Paydar,
Volume 28, Issue 3 (9-2017)
Abstract

Nowadays, the necessity of manufacturers’ response to their customers’ needs and their fields of activities have extended widely. The cellular manufacturing systems have adopted reduced costs from mass-production systems and high flexibility from job-shop manufacturing systems, and therefore, they are very popular in modern manufacturing environments. Manufacturing systems, in addition to proper machinery and equipment, workforces and their performance play a critical role.

Staff creativity is an important factor in product development, and their interest in cooperating with each other in the work environment can help the growth and maturity of this factor. In this research, two important aspects of cellular manufacturing take into consideration: Cell formation and workforce planning. Cell formation is a strategic decision, and workforce planning is a tactical decision. Practically, these two sectors cannot be planned simultaneously, and decision making in this regard is decentralized. For this reason, a bi-level mathematical model is proposed. The first level aims to reduce the number of voids and exceptional elements, and the second level tends to promote the sense of interest between the workforces for working together, which will result in synergy and growth of the organization.


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.

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