• 대한산업공학회지
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• 제24권4호
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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.

• 한국통신학회논문지
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• 제33권8A호
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• pp.794-804
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• 2008

본 논문에서는 EPCglobal Class-1 Generation-2 (Gen2) 기반 Probabilistic Slotted 충돌방지 알고리즘에 대하여 살펴보고, 태그인식시간, 충돌 비율을 감소시키고, 데이터 처리량, 시스템 효율을 증가 시킬 수 있는 QueryAdjust 명령어를 사용한 FAFQ (fixed adjustable framed Q) 알고리즘과 AAFQ (adaptive adjustable framed Q) 알고리즘을 제안하며, 또한 Gen2 기반으로 태그 인식 효율을 향상 시킬 수 있는 Grouping 방법을 제안한다. 제안한 방법들 모두 Q 알고리즘의 성능 향상을 보이며, 제안하는 방법 중 AAFQ 알고리즘이 가장 높은 성능 향상을 나타낸다. 즉, AAFQ 알고리즘에 의하여 5% 정도의 시스템 효율 성능 향상과 4.5% 정도의 충돌 비율 감소를 얻을 수 있다. Grouping 방법은 FAFQ 알고리즘과 AAFQ 알고리즘에 대해선 Ungrouping 방법과 비슷한 성능을 보이지만, Gen2 Q 알고리즘의 경우 Ungrouping 방법과 비교 하였을 때 태그인식시간 및 충돌 비율을 감소시키고, 데이터 처리량 및 시스템 효율을 증가 시킨다.

• 산업공학
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• 제9권2호
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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.

• 산업경영시스템학회지
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• 제18권34호
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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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• 제10권1호
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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.

• 산업경영시스템학회지
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• 제19권37호
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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.

• 산업경영시스템학회지
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• 제39권3호
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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.

• 산업경영시스템학회지
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• 제23권61호
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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.

• 대한산업공학회지
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• 제17권1호
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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.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권1호
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• pp.82-88
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• 2004

프로그램 과제물과 같은 많은 프로그램을 모두 일일이 비교하는 것은 비용이 많이 든다. 더군다나 검수자가 과제물을 검사한다든가, 점수를 부여하고자 한다면 더욱 많은 시간이 요구된다. 물론 검수자가 많은 시간을 두고 평가해도 객관성이 떨어질 수도 있다. 이러한 문제점은 프로그램 과제물에 대해서 유사한 프로그램으로 서로 묶어 놓는다면 쉽게 해결할 수 있다. 즉, 유사한 프로그램으로 서로 묶어놓고 검사한다면 쉽게 검사나 평가가 가능하다. 본 논문에서는 많은 프로그램에 대해서 유사성이 높은 프로그램으로 그룹 짓기(grouping)를 수행하는 알고리즘을 제시하고 구현한다. 그룹 짓기 알고리즘은 (9)에서 제시한 프로그램 유사도 평가 알고리즘을 이용하여 유사도를 측정한 후, 유사성이 높은 프로그램을 그룹 짓기를 수행한다. 이 그룹 짓기 알고리즘을 이용하면 n개의 프로그램에 대해서 최대 n(n-1)/2 번에서 최소 (n-1)번까지 비교 횟수를 줄일 수가 있다. 본 논문의 실험 및 평가 부분에서는 실제로 모 대학의 과제물 10개를 추출하여 유사성을 기준으로 실험 평가한 결과를 보여준다.