Group Technology Cell Formation Research Articles

Meta-heuristics in cellular manufacturing: A state-of-the-art review

Article history: Received 1 April 2010 Received in revised form 22 July 2010 Accepted 30 July 2010 Available online 1 Auguest 2010 Meta-heuristic approaches are general algorithmic framework, often nature-inspired and designed to solve NP-complete optimization problems in cellular manufacturing systems and has been a growing research area for the past two decades. This paper discusses various metaheuristic techniques such as evolutionary approach, Ant colony optimization, simulated annealing, Tabu search and other recent approaches, and their applications to the vicinity of group technology/cell formation (GT/CF) problem in cellular manufacturing. The nobility of this paper is to incorporate various prevailing issues, open problems of meta-heuristic approaches, its usage, comparison, hybridization and its scope of future research in the aforesaid area. © 2010 Growing Science Ltd. All rights reserved.

Complete and fractional cell formation using Kohonen self-organizing map networks in a cellular manufacturing system

The primary objective of group technology (GT) is to enhance the productivity in batch manufacturing environment. The GT cell formation and fractional cell formation are done by using Kohonen self-organizing map (KSOM) networks. The effectiveness of the cell formation is measured with number of exceptional elements, bottleneck parts and grouping efficiency and the effectiveness of the fractional cell formation is measured by number of exceptional elements and the number of machines in the reminder cell. This method is applied to the known benchmarked problems found in the literature and it is found to be equal or best when compared to the other algorithms in terms of minimizing the number of the exceptional elements. The relative merits of using this method with respect to other known algorithms/heuristics in terms of computational speed and consistency are presented.

Manufacturing cell formation using modified ART1 networks

The primary objective of group technology (GT) is to enhance the productivity in the batch manufacturing environment. The GT cell formation problem is solved using modified binary adaptive resonance theory networks known as ART1. The input to the modified ART1 is a machine-part incidence matrix comprised of the binary digits “0” and “1”. And the outputs are the list of part families and the corresponding part list, machine cells and their corresponding list of machines, and the number of exceptional elements. This method is applied to the known benchmarked problems found in the literature and it is found to outperform other algorithms in terms of minimizing the number of the exceptional elements. The relative merits of using this method with respect to other known algorithms/heuristics in terms of computational speed and consistency are presented .

Fractional cell formation in group technology using modified ART1 neural networks

Group technology (GT) is a manufacturing philosophy that attempts to reduce production cost by reducing the material handling and transportation cost. The GT cell formation by any known algorithm/heuristics results in much intercell movement known as exceptional elements. In such cases, fractional cell formation using reminder cells can be adopted successfully to minimize the number of exceptional elements. The fractional cell formation problem is solved using modified adaptive resonance theory1 network (ART1). The input to the modified ART1 is machine-part incidence matrix comprising of the binary digits 0 and 1. This method is applied to the known benchmarked problems found in the literature and it is found to be equal or superior to other algorithms in terms of minimizing the number of the exceptional elements. The relative merits of using this method with respect to other known algorithms/heuristics in terms of computational speed and consistency are presented.

Modified p-median approach for efficient GT cell formation

The objective of this paper is to propose the modified formulations over the classical p-median model for efficiently solving the cell formation (CF) problem with which production managers are faced in implementing group technology (GT) to manufacturing. In spite of its merits as the robust solution approach to GT cell formation, the classical p-median model is limited to small-size CF problems since it requires many binary variables. Necessity of many binary variables makes it difficult for cell designer to use the classical p-median model for solving even medium-sized CF problems, let alone large-size CF problems even though large-memory main frame computer and highly expensive commercial software are used. To resolve this sort of problems, we propose two modified p-median formulations which are even applicable to largest CF problems among the ones found in literature. Their main virtues are as follows. First, the modified p-median formulations encourage speedy implementation of the models. Secondly, the proposed formulations that are applied to moderately large-size CF problems with 30 or more machines can run effectively even on personal computer and education-purpose softwares such as the HYPER LINDO which are available at such a low cost compared with commercial software. Computational results from applying to moderately large-size CF problems with 30 to 50 machines show that the CF using the modified p-median formulations provides very promising implications as compared with the CF using the classical p-median model.

The trade-off between intracell and intercell moves in group technology cell formation

This paper presents a new method of generating adaptive ruled layers for rapid prototyping (RP) processing of complex parts. To increase the accuracy and reduce the build time, an adaptive ruled layer approximation of the stereolithography (STL) models and multiaxis path planning for rapid prototyping are proposed. In this paper, a new method of constructing ruled layers from slicing STL points is developed to approximate the STL models with better surface accuracy. A technique of surface error analysis is presented to find the maximum errors at different layers of RP parts. By finding the RP surface errors, adaptive ruled layers are generated for the RP process of the STL CAD models. Using the constructed ruled layers of the STL models, the multiaxis material removal process is integrated with traditional RP processes to achieve better surface accuracy and to reduce the total RP build time. Computer implementation and illustrative examples are presented.

Group technology cell formation considering operation sequences and production volumes

In this paper a new approach for minimizing intercell part flows in cellularmanufacturing is proposed. Two types of production data-based part-machine incidence matrices (PMIMs) are developed to reflect the real-field data such as the operation sequences and production volumes of the parts to be manufactured. The type I production data-based PMIM represents the actual flows to or from machines by parts. A 0?1 linear formulation using the type I production data-based PMIM is developed to minimize the total intercell flows. Once the type I production data-based PMIM is obtained, a type II production data-based PMIM is constructed to reflect the actual intercell flows by bottleneck parts requiring operations outside its corresponding cell. Computational results with the well known data sets in literature show that the proposed production data-based PMIMs can be used as the fundamental replacements over the classical binary PMIM in the cell formation problem, and the mathematical model based on the new PMIMs reflecting the real-field data can be applied effectively to proper medium-sized cell formation problems.

The trade-off between intracell and intercell moves in group technology cell formation

Abstract In cellular manufacturing, machines are organized into compact and independent product-based group technology (GT) cells. Production in smaller GT cells not only reduces travel distances but also facilitates better material and production control. Cell compactness and cell independence are two basic requirements of efficient GT cells. Most cell formation procedures consider maximizing cell independence as the objective. In doing so, these procedures impose an upper limit on cell size as a threshold value for cell compactness. This paper views cell compactness and cell independence in terms of intercell and intracell move costs. The trade-off between intracell and intercell move costs is explained in detail. A nonlinear mathematical model and simulated annealing algorithm are developed that minimize the total intracell and intercell move costs. In the calculation of move costs, production quantity, effect of cell size on intracell move, effect of sequence of operations, and multiple non-consecutive visits to the same machine are considered. The results are compared with published results.

Performance of Fuzzy ART neural network for group technology cell formation

This study investigates the performance of Fuzzy ART neural network for grouping parts and machines, as part of the design of cellular manufacturing systems. Fuzzy ART is compared with ART1 neural network and a modification to ART1, along with direct clustering analysis (DCA) and rank order clustering (ROC2) algorithms. A series of replicated clustering experiments were performed, and the efficiency and consistency with which clusters were identified were examined, using large data sets of differing sizes and degrees of imperfection. The performance measures included the recovery ratio of bond energy and execution times, It is shown that Fuzzy ART neural network results in better and more consistent identification of block diagonal structures than ART1, a recent modification to ART1, as well as DCA and ROC2. The execution times were found to be more than those of ART1 and modified ART1, but they were still superior to traditional algorithms for large data sets.

The cost of eliminating exceptional elements in group technology cell formation

Abstract Many researchers have suggested methods for the formation of machine cells/part families in group technology. However, few of these methods have addressed the possible existence of exceptional elements (EE) in a reasonable manner. EE can be the result of bottleneck machines whose processing is needed by parts assigned to more than one part family. They can also be caused by parts that require processing on machines assigned to more than one machine cell. The existence of EE in cell formation solutions is a nontrivial problem that requires interaction between machine cells intended to be independent for production efficiency. This paper presents a systematic method for identifying opportunities for reducing the number of intercell transfers caused by the existence of EE. The method recognizes how each EE in a cell formation solution may be involved in the creation of intercell transfers. The sequence of operation in each part routeing is also considered. The method then analyses the costs associat...

Modelling group technology cell formation as a generalized assignment problem

SUMMARY The formation of part families and technology cells is a step in implementing Group Technology, In this paper we show that the simple cell-formation problem is equivalent to the Generalized Assignment Problem (GAP). Furthermore, we show that the general case of cell formation, in which several process plans are considered for each part type, is also equivalent to the GAP.

Group technology cell formation — Some new insights

Abstract A new technique for designing manufacturing cells based on the hospitality and flexibility of jobs are used. Host cells are formed and further reduction in the number of cells are achieved using the integer programming technique. An example problem has been solved using this approach. Cost data are included in the example problem for evaluating the different designs.