• Journal of Korean Institute of Industrial Engineers
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• v.31 no.1
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• pp.20-26
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• 2005

This paper proposes the heuristic algorithm for the generalized GT problems to consider the restrictions which are given the number of machine cell and maximum number of machines in machine cell as well as minimum number of machines in machine cell. This approach is split into two phase. In the first phase, we use the similarity coefficient which proposes and calculates the similarity values about each pair of all machines and sort these values descending order. If we have a machine pair which has the largest similarity coefficient and adheres strictly to the constraint about birds of a different feather (BODF) in a machine cell, then we assign the machine to the machine cell. In the second phase, we assign parts into machine cell with the smallest number of exceptional elements. The results give a machine-part grouping. The proposed algorithm is compared to the Modified p-median model for machine-part grouping.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.7-13
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• 2017

In this paper, we propose a novel dynamic subchannel grouping (DSG) algorithm to maximize the system capacity considering intended proper outage probability for the downlink of enterprise small-cell networks (ESNs). In the proposed DSG scheme, a local gateway (LGW) which is installed in a building dynamically divides the frequency bandwidth into different numbers of subchannel groups (SGs) based on the numbers of small-cell access points (SAPs) and small-cell user equipments (SUEs) per floor. Then, the LGW assigns the SGs to SAPs and the SAPs allocate them to their serving SUEs. Through simulation results, we show that the proposed DSG scheme is appropriate for the ESNs compared to the conventional small-cell networks in which all SAPs use the number of fixed SGs in terms of the system capacity and outage probability.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.259-262
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• 2003

Group technology(GT) is a manufacturing philosophy which identifies and exploits the similarity of parts and processes in design and manufacturing. A specific application of GT is cellular manufacturing. the first step in the preliminary stage of cellular manufacturing system design is cell formation, generally known as a machine-part cell formation(MPCF). This paper presents and tests a grouping gentic algorithm(GGA) for solving the MPCF problem and uses the measurements of e(ficacy. GGA's replacement heuristic used similarity coefficients is presented.

• IE interfaces
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• v.12 no.4
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• pp.532-539
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• 1999

The first step to design a cellular manufacturing system is to make part-families and machine-cells. This process is called the machine-part grouping. This paper considers a machine-cell size when grouping machine-cells. By considering a machine-cell size, an unrealistically big size of machine-cell which may be caused by the chaining effect can be avoid. A heuristic algorithm which minimizes the number of inter-cell movement of parts considering a machine-cell size is presented. The effectiveness and performance of the proposed heuristic algorithm are compared with those of several heuristic algorithms previously reported. The comparison shows that the proposed heuristic algorithm is efficient and reliable in minimizing the number of inter-cell movement of parts and also prevents the chaining effect.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.1
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• pp.1-9
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• 2005

This paper proposes the heuristic algorithm for the generalized GT problems to consider the restrictions which are given the number of machine cell and maximum number of machines in machine cell as well as minimum number of machines in machine cell. This approach is split into two phase. In the first phase, we use the similarity coefficient which proposes and calculates the similarity values about each pair of all machines and sort these values descending order. If we have a machine pair which has the largest similarity coefficient and adheres strictly to the constraint about birds of a different feather (BODF) in a machine cell, then we assign the machine to the machine cell. In the second phase, we assign parts into machine cell with the smallest number of exceptional elements. The results give a machine-part grouping. The proposed algorithm is compared to the Modified p-median model for machine-part grouping.

• Korean Management Science Review
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• v.24 no.1
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• pp.91-111
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• 2007

In this paper an effective two-phase approach adopting modified p-median mathematical model is proposed for grouping machines and parts in cellular manufacturing(CM). Unlike the conventional methods allowing machines and parts to be improperly assigned to cells and families, the proposed approach seeks to find the proper block diagonal solution where all the machines and parts are properly assigned to their most associated cells and families in term of the actual machine processing and part moves. Phase 1 uses the modified p-median formulation adopting new inter-machine similarity coefficient based on the non-binary production data-based part-machine incidence matrix(PMIM) that reflects both the operation sequences and production volumes for the parts to find machine cells. Phase 2 apollos iterative reassignment procedure to minimize inter-cell part moves and maximize within-cell machine utilization by reassigning improperly assigned machines and parts to their most associated cells and families. Computational experience with the data sets available on literature shows the proposed approach yields good-quality proper block diagonal solution.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.3
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• pp.213-221
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• 2003

In this paper, we consider a multi-objective part-machine grouping problem, in which part types have several alternative part routings and each part routing has a machining sequence. This problem is characterized as optimally determining part type sets and its corresponding machine cells such that the sum of inter-cell part movements and the sum of machine workload imbalances are simultaneously minimized. Due to the complexity of the problem, a two-stage heuristic algorithm is proposed, and experiments are shown to verify the effectiveness of the algorithm.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.130-134
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• 2008

The increasing size of very large scale integration (VLSI) circuits, high transistor density, and popularity of low-power circuit and system design are making the minimization of power dissipation an important issue in VLSI design. Test Power dissipation is exceedingly high in scan based environments wherein scan chain transitions during the shift of test data further reflect into significant levels of circuit switching unnecessarily. Scan chain or cell modification lead to reduced dissipations of power. The ETC algorithm of previous work has weak points. Taking all of this into account, we therefore propose a new algorithm. Its name is RE_ETC. The proposed modifications in the scan chain consist of Exclusive-OR gate insertion and scan cell reordering, leading to significant power reductions with absolutely no area or performance penalty whatsoever. Experimental results confirm the considerable reductions in scan chain transitions. We show that modified scan cell has the improvement of test efficiency and power dissipations.

• Journal of the Korea Society of Computer and Information
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• v.21 no.2
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• pp.71-77
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• 2016

There has been unknown polynomial time algorithm for cellular formation problem (CFP) that is one of the NP-hard problem. Therefore metaheuristic method has been applied this problem to obtain approximated solution. This paper shows the existence of polynomial-time heuristic algorithm in CFP. The proposed algorithm performs coarse-grained and fine-grained cell formation process. In coarse-grained cell formation process, the cell can be formed in accordance with machine frequently used that is the number of other products use same machine with special product. As a result, the machine can be assigned to most used cell. In fine-grained process, the product and machine are moved into other cell that has a improved grouping efficiency. For 35 experimental data, this heuristic algorithm performs better grouping efficiency for 12 data than best known of meta-heuristic methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.46-53
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• 2016

In mobile communication systems, location management deals with the location determination of users in a network. One of the strategies used in location management is to partition the network into location areas. Each location area consists of a group of cells. The goal of location management is to partition the network into a number of location areas such that the total paging cost and handoff (or update) cost is a minimum. Finding the optimal number of location areas and the corresponding configuration of the partitioned network is a difficult combinatorial optimization problem. This cell grouping problem is to find a compromise between the location update and paging operations such that the cost of mobile terminal location tracking is a minimum in location area wireless network. In fact, this is shown to be an NP-complete problem in an earlier study. In this paper, artificial bee colony (ABC) is developed and proposed to obtain the best/optimal group of cells for location area planning for location management system. The performance of the artificial bee colony (ABC) is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters. The important control parameter of ABC is only 'Limit' which is the number of trials after which a food source is assumed to be abandoned. Simulation results for 16, 36, and 64 cell grouping problems in wireless network show that the performance of our ABC is better than those alternatives such as ant colony optimization (ACO) and particle swarm optimization (PSO).