• International Journal of Quality Innovation
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• v.2 no.2
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• pp.101-120
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• 2001

In this paper jobshop scheduling problem was considered on automated manufacturing systems with the closed loop and unidirectional material handling system. The objective of this research is to develop and evaluate heuristic scheduling procedures to improve productivity by minimizing makespan. Especially travel time of material handling system as well as processing time was considered in the proposed algorithms, A new heuristic algorithms are proposed and illustrates the proposed algorithm. The heuristic algorithms are implemented for various cases. The results show that the proposed algorithms provide better solutions in productivity, frequency, job waiting time and the number of waiting jobs than the random scheduling algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.64
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• pp.1-20
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• 2001

In distributed databases, file replication and workload allocation are important design issues. This paper solves these two issues simultaneously, The primary objective is to minimize the system response time that consists of local processing and communication overhead on a local area network. Workload (query transactions) is assigned among any sites in proportion to the remaining file request service rate of the each server The problem is presented in the form of a nonlinear integer programming model. The problem is proved to be NP-complete and thus an efficient heuristic is developed by employing its special structure. To illustrate its effectiveness, it is shown that the proposed heuristic is based on the heuristic of a non-redundant allocation that was provided to be effective. The model and heuristics are likely to provide more effective distributed database designs.

• The Transactions of the Korea Information Processing Society
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• v.5 no.9
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• pp.2353-2360
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• 1998

In the past. as the environment of the established soft ware system changed, most Re-Engineering perforned clustering on the basis of logical operation, In contrast, this paper proposes a method to perfonn clustering efficiently using the infonmltion sharing of each modult, of source programs that constitute the software For the clustering of related modules using the information sharing. We evaluated the result after measuring the degree of clustering using similarity and uniqueness algorithm on the basis of heuristic method of measurement. Thus, we could manipulate and achieve the clustering of related modules and procedures, This paper also prests a method to reconstruct the software system efficiently through the clustering and shows the possibility of its realization through real example.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.389-398
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• 2010

The Vehicle Routing and Scheduling Problem with Time Windows(VRSPTW) is to establish a delivery route of minimum cost satisfying the time constraints and capacity demands of many customers. The VRSPTW takes a long time to generate a solution because this is a NP-hard problem. To generate the nearest optimal solution within a reasonable time, we propose the heuristic by using an ACO(Ant Colony Optimization) with multi-cost functions. The multi-cost functions can generate a feasible initial-route by applying various weight values, such as distance, demand, angle and time window, to the cost factors when each ant evaluates the cost to move to the next customer node. Our experimental results show that our heuristic can generate the nearest optimal solution more efficiently than Solomon I1 heuristic or Hybrid heuristic applied by the opportunity time.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1367-1369
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• 2013

Applying Application-Level Multicast technology (ALM) to Distributed Interactive Applications (DIAs) becomes more and more popular. Especially for DIAs embedded priority that the sender forwards data to receivers due to their respective priorities. The priority-based directed minimum spanning tree (PST) algorithm was designed for these DIAs. However, the PST has no efficient priority selection and filtering mechanism. The system will consume a tremendous amount of resource for reconstructing distribution tree and becomes unstable and unscalable. In this paper, First, We propose a novel priority-based application level multicast algorithm: Predict-and-Quantize for Priority with directed minimum Spanning Tree (PQPST), which can efficiently predict efficient priorities for the receivers and quantize the predicted priorities to build the multicast distribution tree. Second, we propose Priority Discrepancy Heuristic Mechanism (PDHM), which sets different thresholds of priority discrepancy within the priority discrepancy interval to control the distribution tree construction can efficiently decrease the repeated distribution tree construction, and we get the best heuristic priority discrepancy interval by PQPST. According to the simulation results, the PQPST and PDHM can efficiently improve the performance of the PST algorithm.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.05a
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• pp.345-349
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• 2005

Massive amount of satellite image processing such asglobal/continental-level analysis and monitoring requires automated and speedy georegistration. There could be two major automated approaches: (1) rigid mathematical modeling using sensor model and ephemeris data; (2) heuristic co-registration approach with respect to existing reference image. In case of ETM+, the accuracy of the first approach is known as RMSE 250m, which is far below requested accuracy level for most of satellite image processing. On the other hands, the second approach is to find identical points between new image and reference image and use heuristic regression model for registration. The latter shows better accuracy but has problems with expensive computation. To improve efficiency of the coregistration approach, the author proposed a pre-qualified matching algorithm which is composed of feature extraction with canny operator and area matching algorithm with correlation coefficient. Throughout the pre-qualification approach, the computation time was significantly improved and make the registration accuracy is improved. A prototype was implemented and tested with the proposed algorithm. The performance test of 14 TM/ETM+ images in the U.S. showed: (1) average RMSE error of the approach was 0.47 dependent upon terrain and features; (2) the number average matching points were over 15,000; (3) the time complexity was 12 min per image with 3.2GHz Intel Pentium 4 and 1G Ram.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.1283-1286
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• 2006

본 논문에서는 관찰된 채널의 유휴 시간을 이용하여, ACK(Acknowledgement) 기반의 링크 적응 기술의 단점을 극복하는 새로운 알고리즘인 COLA 를 제안한다. COLA 는 광범위한 채널과 부하 상황에서 최적의 성능을 얻으며 RTS/CTS 메커니즘이나 하드웨어를 이용한 채널 SNR 측정과 같은 추가적인 옵션 혹은 별도의 프로토콜 메커니즘의 지원이 필요하지 않다. 또한 COLA 알고리즘은 단지 짧은 10 개의 critical 한 명령어를 사용함으로써 부하를 줄이며, 실제 동작 시 수반되는 heuristic 한 매개변수로부터 자유롭고, 따라서 실제 사용 시 장점을 가진다.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.569-589
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• 2018

Cloud computing, also known as "country as you go", is used to turn any computer into a dematerialized architecture in which users can access different services. In addition to the daily evolution of stakeholders' number and beneficiaries, the imbalance between the virtual machines of data centers in a cloud environment impacts the performance as it decreases the hardware resources and the software's profitability. Our axis of research is the load balancing between a data center's virtual machines. It is used for reducing the degree of load imbalance between those machines in order to solve the problems caused by this technological evolution and ensure a greater quality of service. Our article focuses on two main phases: the pre-classification of tasks, according to the requested resources; and the classification of tasks into levels ('odd levels' or 'even levels') in ascending order based on the meta-heuristic "Bat-algorithm". The task allocation is based on levels provided by the bat-algorithm and through our mathematical functions, and we will divide our system into a number of virtual machines with nearly equal performance. Otherwise, we suggest different classes of virtual machines, but the condition is that each class should contain machines with similar characteristics compared to the existing binary search scheme.

• Journal of KIISE
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• v.43 no.2
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• pp.229-236
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• 2016

Theta join is one of the essential and important types of queries in database systems. As the amount of data needs to be processed increases, processing theta joins with a single machine becomes impractical. Therefore, theta join algorithms using distributed computing frameworks have been studied widely. Although one of the state-of-the-art theta-join algorithms uses M-Bucket-I heuristic, it is hard to use since running time of M-Bucket-I heuristic, which computes a mapping from a record to a reducer (i.e., reducer mapping), is O(n) where n is the size of input data. In this paper, we propose MBI-I algorithm which reduces the running time of M-Bucket-I heuristic to $O(r_{max}log\;n)$ and gives the same result as M-Bucket-I heuristic does. We also conducted several experiments to show algorithm and confirmed that our algorithm can improve the performance of a theta join by 10%.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.360-376
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• 2020

Many heuristic attribute reduction algorithms have been proposed to find a single reduct that functions as the entire set of original attributes without loss of classification capability; however, the proposed reducts are not always perfect for these multiclass datasets. In this study, based on a probabilistic rough set model, we propose the class-oriented attribute reduction (COAR) algorithm, which separately finds a reduct for each target class. Thus, there is a strong dependence between a reduct and its target class. Consequently, we propose a type of ensemble constructed on a group of classifiers based on class-oriented reducts with a customized weighted majority voting strategy. We evaluated the performance of our proposed algorithm based on five real multiclass datasets. Experimental results confirm the superiority of the proposed method in terms of four general evaluation metrics.