• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.25
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• pp.83-90
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• 1992

This paper aims at the problem that design the network of Rig-star type with the minimum cost, which the Ring is composed of the selected nodes(concentrators) among the several candidate nodes on the network and other nodes(terminals) is connected to the Ring by star subnetwork. Especially, we consider the terminal reliability in network design problem. We develop the heuristic algorithm for network design problem to obtain the near optimal(best) solution for problem. We use an add-heuristic method and 2-exchange method in developing the heuristic algorithm.

• Korean Management Science Review
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• v.21 no.1
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• pp.77-86
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• 2004

In this paper, we present a methodology for solving the multicommodity network flow problems using interior point methods. In our method, the minimum cost network flow problem extracted from the given multicommodity network flow problem is solved by primal-dual barrier method in which normal equations are solved partially using preconditioned conjugate gradient method. Based on the solution of the minimum cost network flow problem, a warm-start point is obtained from which Castro's specialized interior point method for multicommodity network flow problem starts. In the computational experiments, the effectiveness of our methodology is shown.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.2
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• pp.87-100
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• 2000

As a result of rapid advances in communication technology, fiber optics have begun to be adopted in most telecommunication systems 3s an economic choice Due to the trend of evolution toward broadband communication network with fiber optics and electronic devices. the network design problem for broadband communication has been received a great deal of research attention recently. In this paper, we address a topological design problem for broadband local access network with uncertain demands, which has received surprisingly little attention so far. in our problem, we select a set of hubs and links for constructing network expected penalty cost for the amount of undersupplied In addition to the usual cost terms of the fixed demand problem Our problem can be approximated as a mixed 0-1 integer programming problem by using Szwarc’s linear approximation technique. Then the problem is transformed successfully into a version of classical network design model. Some computational experiments for the model and concluding remarks are described.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.133-138
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• 1986

In this paper, we are modeling the problem of the reliability evaluation in the network with delay. The triconnected decomposition and factoring algorithm for the network reliability, known as the most efficient algorithm, does not work in this constrained problem. So, we propose some ideas that reduce the above constrained problem to the general network reliability problem. We also present an algorithm for the reliability evaluation in the network with delay based on these ideas.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.42.2-42
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• 2002

We discuss a neural network solver for the inverse optimization problem. The problem is that find functional relations between input and output data, which are include defects. Finding the relations, predictions of the defect parts are also required. The part of finding the defects in the input data is an inverse problem . We consider the meanings to solve the problem on the neural network system at first. Next, we consider the network structure of the system, the learning scheme of the network, and at last, examine the precision on the numerical calculations. In the paper, we proposed the high-precision learning method for plural three-layer neural network system that is series-connect...

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.2
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• pp.1-19
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• 1994

A generalized network problem is a special class of linear programming problem whose coefficient matrix contains at most two nonzero elements per column. A generalized network problem with 0-1 flow restrictions is called an integer generalized network(IGN) problem. In this paper, we presented a branch and bound algorithm for the IGN that uses network relaxation. To improve the procedure, we develop various strategies, each of which employs different node selection criterion and/or branching variable selection criterion. We test these solution strategies and compare their efficiencies with LINDO on 70 randomly generated problems.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.1
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• pp.67-84
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• 2000

In this paper, we address a network design problem including the decision of hub facility locatiions which is typically found in a communicatio and a transportation network design studies. Due to the administrative and the geographical restrictions, it is common to assume that each user should be assigned to only one hub facility. To construct such a network, three types of network costs should be considered: the fixed costs of establishing the hubs and the arcs in the network, and the variable costs associated with transversing the network. The complex problem is formulated as a mixed IP embedding a multicommodity flow problem. Exploiting its special structure, a dual-based heuristic is then developed, which yields near-optimal design plans. The test results indicate that the heuristic is an effective way to solve this computationally complex problem.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.195-204
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• 2003

Network optimization is being increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. Networks provide a useful way to modeling real world problems and are extensively used in practice. Many real world applications impose on more complex issues, such as, complex structure, complex constraints, and multiple objects to be handled simultaneously and make the problem intractable to the traditional approaches. Recent advances in evolutionary computation have made it possible to solve such practical network optimization problems. The invited talk introduces a thorough treatment of evolutionary approaches, i.e., hybrid genetic algorithms approach to network optimization problems, such as, fixed charge transportation problem, minimum cost and maximum flow problem, minimum spanning tree problem, multiple project scheduling problems, scheduling problem in FMS.

• Management Science and Financial Engineering
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• v.6 no.2
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• pp.29-45
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• 2000

We consider the design of a two-level telecommunication network having logical full-mesh/star topology, with the implementation of conduit systems taken together. The design problem is then viewed as consisting of three subproblems: locating hub facilities, placing a conduit network, and installing cables therein to configure the logical full-mesh/star network. Without partitioning into subproblems as done in the conventional approach, the whole problem is directly dealt with in a single integrated framework, inspired by some recent successes with the approach. We successfully formulate the problem as a variant of the classical multicommodity flow model for the fixed charge network design problem, aided by network augmentation, judicious commodity definition, and some flow restrictions. With our optimal model, we solve some randomly generated sample problems by using CPLEX MIP program. From the computational experiments, it seems that our model can be applied to the practical problem effectively.

• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.12-12
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• 1994

Simulation output knowledge analysis is one of problem-solving and/or knowledge adquistion process by investgating the system behavior under study through simulation . This paper describes an approach to simulation outputknowldege analysis using fuzzy neural network model. A fuzzy neral network model is designed with fuzzy setsand membership functions for variables of simulation model. The relationship between input parameters and output performances of simulation model is captured as system behavior knowlege in a fuzzy neural networkmodel by training examples form simulation exepreiments. Backpropagation learning algorithms is used to encode the knowledge. The knowledge is utilized to solve problem through simulation such as system performance prodiction and goal-directed analysis. For explicit knowledge acquisition, production rules are extracted from the implicit neural network knowledge. These rules may assit in explaining the simulation results and providing knowledge base for an expert system. This approach thus enablesboth symbolic and numeric reasoning to solve problem througth simulation . We applied this approach to the design problem of broadband communication network.