• Journal of Korean Institute of Industrial Engineers
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• v.24 no.4
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• pp.649-660
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• 1998

Steel making in Mini Mill consists of three major processing stages: molten steel making in an electric arc fuenace, slab casting in a continuous caster, and hot rolling in a finishing mill. Each processing stage has its own lot grouping criterion. However, these criteria in three stages are conflicting with each other. Therefore, delveloping on efficient lot grouping algorithm to enhance the overall productivity of the Mini Mill is an extremely difficult task. The algorithm proposed in this paper is divided into three steps hierarchically: change grouping, cast grouping, and roll grouping. An efficient charge grouping heuristic is developed by exploiting the characteristics of the orders, the processing constraints and the requirements for the downstream stages. In order to maximaize the productivity of the continuous casters, each cast must contain as many charges as possible. Based on the constraint satisfaction problem technique, an efficient cast grouping heuristic is developed. Each roll consists of two casts satisfying the constraints for rolling. The roll grouping problem is formulated as a weighted non-bipartite matching problem, and an optimal roll grouping algorithm is developed. The proposed algorithm is programmed with C language and tested on a SUN Workstation with real data obtained from the H steel works. Through the computational experiment, the algorithm is verified to yield quite satisfactory solutions within a few minutes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8A
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• pp.794-804
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• 2008

In this paper, we analyze the performance of probabilistic slotted anti-collision algorithm used in EPCglobal Class-1 Generation-2 (Gen2). To increase throughput and system efficiency, and to decrease tag identification time and collision ratio, we propose new tag anti-collision algorithms, which are FAFQ (fired adjustable flamed Q) algorithm and AAFQ (adaptive adjustable framed Q) algorithm, by using QueryAdjust command. We also propose grouping method based on Gen2 to improve the efficiency of tag identification. The simulation results show that all the proposed algorithms outperform Q algorithm, and AAFQ algorithm performs the best. That is, AAFQ has an increment of 5% of system efficiency and a decrement of 4.5% of collision ratio. For FAFQ and AAFQ algorithm, the performance of grouping method is similar to that of ungrouping method. However, for Q algorithm in Gen2, grouping method can increase throughput and system efficiency, and decrease tag identification time and collision ratio compared with ungrouping method.

• IE interfaces
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• v.9 no.2
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• pp.39-45
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• 1996

This paper investigates the problems of grouping N products on an assembly line with an objective of maximizing the option grouping rate. Before developing a mixed model grouping algorithm, simulation studies are committed for developing operating rules and evaluating the layout production systems. A mixed model grouping algorithm is suggested and it is applied to the color selection lane in automobile production system, which reveals a high mixed model grouping rate.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.34
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• pp.123-128
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• 1995

The grouping of parts into families and machines into cells poses an important problem in the design and planning of the flexible manufacturing system(FMS). This paper proposes a new optimal algorithm of forming machine-part groups to maximize the similarity, based on branching from seed machine and bounding on a completed part. This algorithm is illustrated with numerical example. This algorithm could be applied to the generalized machine-part grouping problem.

• Journal of information and communication convergence engineering
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• v.10 no.1
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• pp.40-44
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• 2012

Data mining, which is attracting public attention, is a process of drawing out knowledge from a large mass of data. The key technique in data mining is the ability to maximize the similarity in a group and minimize the similarity between groups. Since grouping in data mining deals with a large mass of data, it lessens the amount of time spent with the source data, and grouping techniques that shrink the quantity of the data form to which the algorithm is subjected are actively used. The current grouping algorithm is highly sensitive to static and reacts to local minima. The number of groups has to be stated depending on the initialization value. In this paper we propose a gene algorithm that automatically decides on the number of grouping algorithms. We will try to find the optimal group of the fittest function, and finally apply it to a data mining problem that deals with a large mass of data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.37
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• pp.1-7
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• 1996

The grouping of parts into families and machines into cells poses an important problem for the improvement of productivity and quality in the design and planning of the flexible manufacturing system(FMS). This paper proposes a new algorithm of forming machine-part groups in case of the bottleneck machine problem and shows the numerical example. This algorithm could be applied to the large scale machine-part grouping problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.83-89
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• 2016

The group formation problem of the machine and part is a critical issue in the planning stage of cellular manufacturing systems. The machine-part grouping with alternative process plans means to form machine-part groupings in which a part may be processed not only by a specific process but by many alternative processes. For this problem, this study presents an algorithm based on self organizing neural networks, so called SOM (Self Organizing feature Map). The SOM, a special type of neural networks is an intelligent tool for grouping machines and parts in group formation problem of the machine and part. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. In the proposed algorithm, output layer in SOM network had been set as one-dimensional structure and the number of output node has been set sufficiently large in order to spread out the input vectors in the order of similarity. In the first stage of the proposed algorithm, SOM has been applied twice to form an initial machine-process group. In the second stage, grouping efficacy is considered to transform the initial machine-process group into a final machine-process group and a final machine-part group. The proposed algorithm was tested on well-known machine-part grouping problems with alternative process plans. The results of this computational study demonstrate the superiority of the proposed algorithm. The proposed algorithm can be easily applied to the group formation problem compared to other meta-heuristic based algorithms. In addition, it can be used to solve large-scale group formation problems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.61
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• pp.137-146
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• 2000

One of the problems faced in implementing cellular manufacturing systems is machine-part group formation. This paper proposes machine-part grouping algorithms based on Self-Organizing Map(SOM) neural networks and K-Means algorithm in cellular manufacturing systems. Although the SOM spreads out input vectors to output vectors in the order of similarity, it does not always find the optimal solution. We rearrange the input vectors using SOM and determine the number of groups. In order to find the number of groups and grouping efficacy, we iterate K-Means algorithm changing k until we cannot obtain better solution. The results of using the proposed approach are compared to the best solutions reported in literature. The computational results show that the proposed approach provides a powerful means of solving the machine-part grouping problem. The proposed algorithm Is applied by simple calculation, so it can be for designer to change production constraints.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.1
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• pp.75-82
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• 1991

This research is concerned with Machine-Part Group Formation(MPGF) methodology for Flexible Manufacturing Systems(FMS). The purpose of the research is to develop a new heuristic algorithm for effectively solving MPGF problem. The new algorithm is proposed and evaluated by 100 machine-part incidence matrices generated. The performance measures are (1) grouping ability of mutually exclusive block-diagonal form. (2) number of unit group and exceptional elements, and (3) grouping time. The new heuristic algorithm has the following characteristics to effectively conduct MPGF : (a) The mathematical model is presented for rapid forming the proper number of unit groups and grouping mutually exclusive block-diagonal form, (b) The simple and effective mathematical analysis method of Rank Order Clustering(ROC) algorithm is applied to minimize intra-group journeys in each group and exceptional elements in the whole group. The results are compared with those from Expert System(ES) algorithm and ROC algorithm. The results show that the new algorithm always gives the group of mutually exclusive block-diagonal form and better results(85%) than ES algorithm and ROC algorithm.

• Journal of KIISE:Software and Applications
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• v.31 no.1
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• pp.82-88
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• 2004

Comparing many programs like programming assignments one by one requires many costs. Moreover, if the checker would evaluate or grade assignments, much more time will be required. Even through the checker invest much time, fairness is not always guaranteed. These problems can be solved easily by grouping similar programs. So, programs after grouping can be easily evaluated and graded. In this paper, we propose and implement algerian performing grouping by similarity on many programs. The grouping algorithm evaluates similarity using algorithm proposed in (9), and performs a grouping following high similarity order. By using this grouping algorithm, the number of comparison among N programs can be reduced from N-1 times to N(N-1)/2 times. In the part of experiment and evaluation of this paper, we actually showed evaluation result by similarity using randomly 10 programming assignments at the university.