Fractional cell formation in group technology

Group Technology (GT) aims at improving productivity in batch manufacturing. Here components are divided into families and machines into cells such that every component in a part family visits maximum number of machines in the assigned cell with an objective of minimizing inter-cell movement. In situations where too many inter-cell moves exist, fractional cell formation using remainder cells can be used. Here, machines are grouped into GT cells and a remainder cell, which functions like a job shop. Component families are formed such that the components visit the assigned cell and the remainder cell and do not visit other cells. The fractional cell formation problem to minimise inter-cell moves is formulated as a linear integer programming problem. Here, movement between machine cells and remainder cells is not counted as inter-cell moves but movement of components among GT cells is considered as inter-cell movement. The fractional cell formation problem is solved using Simulated Annealing. A heuristic algorithm is developed to solve large sized GT matrices. These have applied to a variety of matrices from GT literature and tested on randomly generated matrices. Computational experiences with the algorithms are presented     

Machine-component cell formation in group technology: MACE

This paper discusses the problem of machine-component cell formation in group technology. Many approaches reported in this area, after Burbidge's pioneering work in production flow analysis, make use of the similarity coefficient of the additive type. This paper presents a heuristic approach based on the similarity coefficient of the product type. The proposed method has also been tested by using the similarity coefficient of additive type. For a large number of problems tested, the method yields a minimum number of exceptional elements. The method is computationally straightforward and is explained through a flowchart.     

Lexicographic fuzzy multi-objective model for minimisation of exceptional and void elements in manufacturing cell formation

This research proposes a lexicographic fuzzy multi-objective model based on perfect grouping for concurrent solving the part-family and machine-cell formation problems in a cellular manufacturing system. New simplified mathematical expressions of exceptional and void elements are proposed, opposing conventional quadratic and absolute functions. The main objectives of the proposed solution model, that is, the minimisation of both the number of exceptional elements and the number of void elements is defined by fuzzy goals as pre-emptive ordering. A lexicographic fuzzy goal model is developed to enhance cell performance and machine utilisation simultaneously. A satisfactory efficient solution can easily be obtained, and alternative solutions can also be generated by capturing flexibility of the proposed fuzzy multi-objective programming model. The formulated model can be solved by existing integer programming solvers. Finally, the evaluation of cell formation problems is briefly discussed to show the performance of the proposed model.     

A general search algorithm for cell formation in group technology

Group Technology (GT) is a manufacturing approach, which organizes and uses the information about an item's similarity (parts and/or machines) to enhance efficiency and effectiveness of batch manufacturing systems. The application of group technology to manufacturing requires the identification of part families and formation of associated machine-cells. One approach is the Similarity Coefficient Method (SCM), an effective clustering technique for forming machine cells. SCM involves a hierarchical machine grouping process in accordance with computed ‘similarity coefficients’. While SCM is capable of incorporating manufacturing data into the machine-part grouping process, it is very sensitive to the data to be clustered (Chan and Milner 1982). It has been argued that for SCM to be meaningful, all machines must process approximately the same numbers of parts (Chan and Milner 1982).We present a new approach, based on artificial intelligence principles, to overcome some of these problems by incorporating an evaluation function into the grouping process. Our goal is to provide a method that is both practical and flexible in its use for the process of cell formation. Our method uses the similarity matrix to generate the feasible machine groups. Then an evaluation function is applied to select a machine-cell arrangement through an iterative process. The approach features a graph-based representation (N-tuple) to represent the problem and illustrate the solution strategies. Also, we develop an algorithm to search for the most promising machine groups from the graph. Compared with Single Linkage Clustering and Average Linkage Clustering approaches, our approach attains comparable or better results     

Machine-component cell formation in group technology: a neural network approach

This paper presents a neural network clustering method for the part-machine grouping problem in group technology. Among the several neural networks, a Carpenter-Grossberg network is selected due to the fact that this clustering method utilizes binary-valued inputs and it can be trained without supervision. It is shown that this adaptive leader algorithm offers the capability of handling large, industry-size data sets due to the computational efficiency. The algorithm was tested on three data sets from prior literature, and solutions obtained were found to result in block diagonal forms. Some solutions were also found to be identical to solutions presented by others. Experiments on larger data sets, involving 10000 parts by 100 machine types, revealed that the method results in the identification of clusters with fast execution times. If a block diagonal structure existed in the input data, it was identified to a good degree of perfection. It was also found to be efficient with some imperfections in the data     

Part-machine cell formation in group technology using a simulated annealing-based meta-heuristic

The part-machine cell formation problem (PMCFP) is a crucial step in the design of a cellular manufacturing system and has received considerable research attention over the last five decades. This study proposes a simulated annealing-based meta-heuristic for solving the PMCFP. The effectiveness of the proposed approach is compared to conventional algorithms across a set of PMCFPs available in the literature. Computational results using four types of performance measures show that the proposed simulated annealing-based meta-heuristic is highly effective by comparison with conventional algorithms for PMCFPs on the same test problems.     

Modelling group technology cell formation as a generalized assignment problem

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.     

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.     

A clustering algorithm for machine cell formation in group technology using minimum spanning trees

I address the machine cell part family formation problem in group technology. The minimum spanning tree (MST) For machines is constructed from which seeds to cluster components are generated. Seeds to cluster machines are obtained from component clusters. The process of alternate seed generation and clustering is continued until feasible solutions are obtained. Edges are removed from the MST to identify alternate starting seeds for clustering. The algorithm is tested with matrices available in the literature. The results compare favourably with existing methods.     

Tabu search approach based on a similarity coefficient for cell formation in generalized group technology

Both a similarity coefficient method (SCM)-based algorithm and meta-heuristics have been widely applied to various cell formation problems; however, few studies have explored the combination of the two methods. This paper addresses a hybrid algorithm, in which, based on the initial solution produced by a new SCM-based hierarchical clustering method, a fast and effective tabu search approach is presented to solve cell formation in group technology (GT). The proposed algorithm is applied to several problems from literature and a group of the randomly generated instances with alternative process plans and compared with simulated annealing (SA) and other TS; the results demonstrate that the proposed algorithm is available and efficient for cell formation in generalized GT.     

Cell formation in group technology: A combinatorial search approach

This paper addresses the cell formation problem in group technology (GT). We develop two heuristic methods for generating solutions to the problem. These methods are based on two powerful combinatorial search methods simulated annealing and tabu search. The performance of the heuristics is examined using randomly generated, published and industry data. The results indicate that the simulated annealing based heuristic is the preferred technique in the context of the problem addressed in this paper. Further, we also demonstrate that the simulated annealing based heuristic generates near-optimal solutions to the cell formation model formulated in this paper.     

The generalized group technology concept

In this paper two classes of clustering models are considered: (1) matrix, and (2) integer programming. The relationship between the matrix model, the p-median model and the classical group technology concept is discussed. A generalized group technology concept, based on generation for one part of a number of different process plans, is proposed. This new concept improves the quality of process (part) families and machine cells. A corresponding integer programming model is formulated. The models discussed are illustrated with numerical examples.     

A process planning-orientated approach to part family formation problem in group technology applications

The paper reports on constrained hierarchical clustering applied to similarity analysis, the grouping of process plans and, consequently, the parts manufactured in accordance with them. It proposes solutions to some important problems such as: determining a metric of process plan similarity evaluation; the study of a cluster-creating technique with two process plans and a rule for singling out the standard plan; the determination of a logical threshold value for partitioning into families. Validation of the method creates a family set. The ‘strong forms’ of this set constitute definitive partition into families of the process plan set.     

Integrated group technology,cell formation,process planning,and production planning with application to the emergency room

In this paper an integrated approach for the formation of parts and machine families in group technology is developed. The integrated approach is used to solve cell formation, process planning, and production planning simultaneously. The given information is part processing sequence, part production volume, part alternative processing plans, and part processing times. The approach is used to determine the machine-part cells and part processing plans, while the total intercell part flow is minimized. Also, the convergence of the algorithm is investigated. The approach goes across and beyond the group technology methods by considering sequencing, production planning, process planning, and part-machine cellular information simultaneously. Two methods are investigated: exact (optimal) and heuristic. The approach first solves an integer programming problem to find processing plans and then uses a procedure to form the machine-part cells. The proposed approach solves the problem iteratively until a set of plans for machine-part cell formations is obtained with minimal intercell part flow or interflow cost. An example is presented to explain the developed approach. Experimental results are also provided. An extension of the approach for solving the operations planning of an emergency room is also covered. In this extension of the approach, the application of cell formation provides a solution to efficiently managing patients and utilizing resources. By grouping patients by their needed medical procedures, time and resource efficiency is accomplished. An application to ER of University Hospitals of Case Western Reserve University is given.     

Modification of alumina scale formation on FeCrAlY alloys by minor additions of group IVa elements

The effect of Ti, Zr and Hf minor additions on the alumina scale formation on a high-purity, FeCrAlY model alloy has been studied. Thermogravimetry at 1,200–1,300 °C in Ar–20%O₂ and two-stage oxidation using ¹⁸O-tracer were combined with characterisation by electron microscopy and sputtered neutral mass spectroscopy. After oxidation, the incorporation of Hf and Zr into the scale was far more substantial than that of Ti. This is explained by the higher thermodynamic stability of the Zr- and Hf-based oxides because the incorporation occurred to a large extent via an internal oxidation process. The scale growth kinetics is accelerated by incorporation of zirconia precipitates that provide short-circuit paths for oxygen diffusion, reduce the scale grain size and cause formation of porosity. In contrast, the incorporation of Hf-containing oxides has no such accelerative effect on the scale growth kinetics.     

超滤技术在制药工业中除热原的应用

介绍了热原的本质，它的定量表示及测定方法．随着膜分离技术的快速发展，采用超滤法去除注射药液中的热原已在医药行业实际应用．列举了国外多项应用实例，并介绍了中国科学院上海原子核研究所生产的卷式超滤装置在药液除热原工艺中的使用情况     

Simultaneous formation of machine and human cells in group technology: a multiple objective approach

Since the advent of group technology (GT) as a primary manufacturing tool for reducing setup times and improving production efficiencies, its central theme has been the grouping of similar parts into part families and machines into machine cells. Although the formation of machine-part manufacturing cells is the essence of GT, its full benefits cannot be gained without forming ‘human’ cells in such a way that machine operators with similar expertise and skills are brought together to produce similar part families. Nevertheless, much of the existing GT literature overlooks the behavioural issues associated with a group of workers in the machine cell. This paper addresses such issues by simultaneously forming both machine and compatible human cells. In so doing, we develop a multiple objective model that enables us to analyse the tradeoff between economic and behavioural benefits.     

Novel chain structures in group VI elements

Recent developments in high-pressure methods and advances in X-ray crystallography have led to a new level of understanding of phase diagrams and structures of materials under pressure. Recently discovered phenomena such as complex phases of alkali metals, incommensurate host-guest structures, and incommensurately modulated structures have rendered obsolete our conventional wisdom about the range of structures possible in the elements. Using new in situ diffraction techniques, we have resolved the long-standing problem of the phase-transition sequence of sulphur in its non-metallic state. We demonstrate that it is very different from that previously proposed, with only two phases stable between 1.5 GPa and 83 GPa (the pressure of metallization), and temperatures from 300 K to 1,100 K. The phases have a triangular chain and a squared chain structure. The same squared chain structure is found in the heavier group VI element selenium.     

Minimising material handling cost in cell formation with alternative processing routes by grouping genetic algorithm

The alternative processing route is one of the important design factors for the cell formation problem (CFP) in cellular manufacturing systems (CMSs). Genetic algorithm (GA) is a popular method for solving the CFPs, because GA is capable of searching large regions of the solution's space while being less susceptible to getting trapped in local optima. However, the disadvantage of classical GAs is that the number of manufacturing cells should be known in advance. Knowing the actual number of manufacturing cells is relatively difficult before the CMS design is determined. Grouping genetic algorithm (GGA) is capable of solving CFP without predetermination of the number of cells, which is introduced by Falkenauer's GGA (1998 Falkenauer, E. 1998. Genetic Algorithms for Grouping Problem, New York: Wiley.  [Google Scholar]). In order to adopt the GGA on CFP with alternative processing routes, we develop a new chromosome representation, a local optimisation algorithm for crossover operator and special mutation operators. These efforts ensure the efficiency of our method and are indicated in the numerical examples, and improved solutions are also obtained in the numerical examples.