• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.66
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• pp.1-10
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• 2001

Simulation-Based Expert System(SIMBES) is a very effective tool to solve complex antral hard problems. The SIMBES model includes a simulator, a feature extractor, a machine learning system, a performance evaluator, and a Knowledge-Based Expert System(KBES). Since SIMBES depends on Problem domains, a schedule-based material requirements planning problem, which is NP-hard, was selected to exemplify the SIMBES model. To implement the SIMBES application in Smalltalk paradigm, a system class hierarchy was constructed. The hierarchy consists of five large classes such as Job Generator, Job Scheduler, Job Evaluator, Inference Engine, and Executive System. Several classes inside these classes were identified. Additionally, instance protocols about all classes have been described in terms of messages and pseudo methods. These protocols can be implemented easily by any other object-oriented languages. Furthermore, these results may be used as a skeletal system to develop a new SIMBES efficiently, especially when the application is related to other scheduling problems.

• Journal of National Security and Military Science
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• s.1
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• pp.345-360
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• 2003

To build an information oriented armed forces, the Korean military telecommunication networks adopt TCP/IP standard communication infrastructures based on ATM packet switched networks. Utilizing this network infrastructure, the Korean armed forces also applies to the areas of battleship management for efficient operation command controls and resource management for efficient resource allocations. In this military communication networks, it is essential to determine the least cost network topology under equal performance and reliability constraints. Basically, this type of communication network design problem is known in the literature as an NP Hard problem. As the number of network node increases, it is very hard to obtain an optimal solution in polynomial time. Therefore, it is reasonable to use a heuristic algorithm which provides a good solution with minimal computational efforts. In this study, we developed a simulated annealing based heuristic algorithm which can be utilized for the design of military communication networks. The developed algorithm provides a good packet switched network topology which satisfies a given set of performance and reliability constraints with reasonable computation times.

• Journal of Korea Multimedia Society
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• v.18 no.12
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• pp.1468-1474
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• 2015

Efficient spectrum sharing is an important issue in Device-to-Device (D2D) communications underlaying cellular networks as it can mitigate the interference to cellular users and improve the performance of the systems. In this paper, we formulate the radio resource allocation in D2D communications as a mixed nonlinear integer programing. We show the formulated problem is NP-hard and thus a polynomial time algorithm to solve is not possible. Since such a problem is very hard to obtain the optimal solution within a short running time, we instead propose a fast heuristic suboptimal algorithm to mitigate the interference caused to cellular users and improve the performance of the systems. Simulation results are provided to evaluate the performance of the proposed algorithm.

• Journal of the military operations research society of Korea
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• v.26 no.2
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• pp.120-130
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• 2000

The purpose of this study is to present a method for determining the k most vital arcs in shortest path problem using an evolutionary algorithm. The problem of finding the k most vital arcs in shortest path problem is to find a set of k arcs whose simultaneous removal from the network causes the greatest increase in the total length of shortest path. Generally, the problem determining the k most vital arcs in shortest path problem has known as NP-hard. Therefore, in order to deal with the problem of real world the heuristic algorithm is needed. In this study we propose to the method of finding the k most vital arcs in shortest path problem using an evolutionary algorithm which known as the most efficient algorithm among heuristics. The method presented in this study is developed using the library of the evolutionary algorithm framework and then the performance of algorithm is analyzed through the computer experiment.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.3
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• pp.125-136
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• 2009

The container packing problem Is one of the traditional optimization problems, which is very related to the knapsack problem and the bin packing problem. In this paper, we deal with the quadratic multiple container picking problem (QMCPP) and it Is known as a NP-hard problem. Thus, It seems to be natural to use a heuristic approach such as evolutionary algorithms for solving the QMCPP. Until now, only a few researchers have studied on this problem and some evolutionary algorithms have been proposed. This paper introduces a new efficient evolutionary algorithm for the QMCPP. The proposed algorithm is devised by improving the original network random key method, which is employed as an encoding method in evolutionary algorithms. And we also propose local search algorithms and incorporate them with the proposed evolutionary algorithm. Finally we compare the proposed algorithm with the previous algorithms and show the proposed algorithm finds the new best results in most of the benchmark instances.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.4
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• pp.133-141
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• 2001

Rapid development in computer and network technology these days has created in environment in which decisions for manufacturing companies can be made in a much broader perspective. Especially, better decisions on production and distribution planning(PDP) problems can be made laking advantage of real time information from all the parties concerned. However, since the PDP problem-a core part of the supply chain management- is known to be the so-called NP-hard problem, so heuristic methods are dominantly used to find out solutions in a reasonable time. As one of those heuristic techniques, many previous studios considered genetic a1gorithms. A standard genetic a1gorithm applies rules of reproduction, gene crossover, and mutation to the pseudo-organisms so the organisms can pass along beneficial and survival-enhancing trails to a new generation. When it comes to representing a chromosome on the problem, it is hard to guarantee an evolution of solutions through classic a1gorithm operations alone, for there exists a strong epitasis among genes. To resolve this problem, we propose a hybrid genetic a1gorithm based on Silver-Meal heuristic. Using IMS-TB(Intelligent Manufacturing System Test-bed) problem sets. the good performance of the proposed a1gorithm is demonstrated.

• Journal of the Korea Society of Computer and Information
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• v.4 no.4
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• pp.40-46
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• 1999

The task scheduling has an effect on system execution time in a precedence constrained task graph onto the multiprocessor system. This problem is known to be NP-hard. many people made an effort to obtain near optimal schedule. We compared modified critical path schedule with many other methods(CP, MH, DL Swapping) For testing this subject, we created randomly a directed acyclic task graph with many root nodes and terminal nodes simulation result convinced for us that the modified critical path algorithm is superior to the other scheduling algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.8
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• pp.1843-1849
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• 2009

In real-time 3D Graphics, pipeline optimization is one of techniques enhancing rendering performance. Pipeline optimization is kind of buffer reordering problem, but it is NP-hard. Therefore techniques that is approximating optimal solution and suitable for real-time 3D graphics are needed. This paper analyze pattern of rendering states changing costs for real-time 3D graphics, and based on this, the algorithm that brings rendering states into line by changing costs is proposed. The proposed technique shows good performance enhancement when costs of some rendering states are much higher than others. Proposed technique shows 2.5 to 4 times better performance than non-ordering algorithm and becomes more faster when rendering costs of a state gets higher.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.6
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• pp.173-178
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• 2014

By exploiting parallel processing, the proposed scheduling scheme enhances energy saving capability of multicore processors for real-time tasks while satisfying deadline and fault tolerance constraints. The scheme searches for a near minimum-energy schedule within a polynomial time, because finding the minimum-energy schedule on multicore processors is a NP-hard problem. The scheme consumes manifestly less energy than the state-of-the-arts method even with low parallel processing speedup as well as with high parallel processing speedup, and saves the energy consumption up to 86%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.253-259
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• 2019

The wedding seating problem(WSP) is to finding a minimum loss of guest relations(sit together preference) with restricted seats of a table for complex guest relation network. The WSP is NP-hard because of the algorithm that can be find the optimal solution within polynomial-time is unknown yet. Therefore we can't solve the WSP not computer-assisted programming but by hand. This paper suggests min-cut rule theory that the two guests with maximum preference can't separate in other two tables because this is not obtains minimum loss of preference. As a result of various experimental, this algorithm obtains proper seating chart meet to the seats of a table constraints.