• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.415-422
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• 2014

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.1-7
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• 2019

It is one of the known methods to obtain the optimal solution using the Ant Colony Optimization Algorithm for the Traveling Salesman Problem (TSP), which is a combination optimization problem. In this paper, we solve the TSP problem by proposing an improved new ant colony optimization algorithm that combines genetic algorithm mutations in existing ant colony optimization algorithms to solve TSP problems in many cities. The new ant colony optimization algorithm provides the opportunity to move easily fall on the issue of developing local optimum values of the existing ant colony optimization algorithm to global optimum value through a new path through mutation. The new path will update the pheromone through an ant colony optimization algorithm. The renewed new pheromone serves to derive the global optimal value from what could have fallen to the local optimal value. Experimental results show that the existing algorithms and the new algorithms are superior to those of existing algorithms in the search for optimum values of newly improved algorithms.

• The Transactions of the Korea Information Processing Society
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• v.6 no.1
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• pp.42-48
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• 1999

Consider the situation that informations necessary to solve a certain problem are distributed among processors on a network. It is called a distributed algorithm that in this situation each processor exchanges the message with adjacent processors to solve the problems. This paper proposes a distributed algorithm to solve the problem that constructs the weighted shortest path tree in an asynchronous network system. In general, a distributed algorithm is estimated by the number of messages(message complexity of the distributed algorithm proposed in this paper are O(n53) and O(nln) respectively. where n is the number of processors on the network.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.4
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• pp.63-71
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• 2009

The traditional portfolio optimization problem is to find an investment plan for securities with reasonable trade-off between the rate of return and the risk. The seminal work in this field is the mean-variance model by Markowitz, which is a quadratic programming problem. Since it is now computationally practical to solve the model, a number of alternative models to overcome this complexity have been proposed. In this paper, among the alternatives, we focus on the Mean Absolute Deviation (MAD) model. More specifically, we developed an algorithm to obtain an optimal portfolio from the MAD model. We showed mathematically that the algorithm can solve the problem to optimality. We tested it using the real data from the Korean Stock Market. The results coincide with our expectation that the method can solve a variety of problems in a reasonable computational time.

• ETRI Journal
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• v.44 no.2
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• pp.244-259
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• 2022

Bicycle- or bike-sharing systems (BSSs) have received increasing attention as a secondary transportation mode due to their advantages, for example, accessibility, prevention of air pollution, and health promotion. However, in BSSs, due to bias in bike demands, the bike rebalancing problem should be solved. Various methods have been proposed to solve this problem; however, it is difficult to apply such methods to small cities because bike demand is sparse, and there are many practical issues to solve. Thus, we propose a demand prediction model using multiple classifiers, time grouping, categorization, weather analysis, and station correlation information. In addition, we analyze real-world relocation data by relocation managers and propose a relocation algorithm based on the analytical results to solve the bike rebalancing problem. The proposed system is compared experimentally with the results obtained by the real relocation managers.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.480-489
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• 2016

Hidden station problem can occur in power-line communication (PLC) networks. A simple solution to the problem has been proposed to use request-to-send (RTS)/clear-to-send (CTS) exchange, but this approach cannot solve the hidden station problem perfectly. This paper revisits the problem for PLC networks and designs a protocol to solve it. We first analyze the throughput performance degradation when the hidden station problem occurs in PLC networks. Then, we propose an interferer aware multiple access (IAMA) protocol to enhance throughput and fairness performances, which uses unique characteristics of PLC networks. Using the RTS/CTS exchange adaptively, the IAMA protocol protects receiving stations from being interfered with neighboring networks. Through extensive simulations, we show that our proposed protocol outperforms conventional random access protocols in terms of throughput and fairness.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.108.3-108
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• 2001

In this work, we consider a real time optimal control problem of mechanical systems with restrictions for actuators i.e. input restrictions and constraints for the movable area i.e. state constraints. First, we formulate an optimal control problem which evaluates the cost function for a finite time horizon with input restrictions and state constraints of a wheeled vehicle as an example of mechanical systems. In this problem, the differentiability of the cost function is not required and this implies that the problem cannot be solved analytically. Therefore, in this work, we use an optimization method to solve the optimal control problem and a new real time optimization method is proposed to solve the problem. In this method, we provide a parameter that indicates the ...

• Korean Management Science Review
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• v.26 no.1
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• pp.65-76
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• 2009

The undirected Steiner tree problem in graphs is known to be NP-hard. The objective of this problem is to find a shortest tree containing a subset of nodes, called terminal nodes. This paper proposes a method based on a two-step procedure to solve this problem efficiently. In the first step. graph reduction rules eliminate useless nodes and edges which do not contribute to make an optimal solution. In the second step, a max-min ant colony optimization combined with Prim's algorithm is developed to solve the reduced problem. The proposed algorithm is tested in the sets of standard test problems. The results show that the algorithm efficiently presents very correct solutions to the benchmark problems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.193-196
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• 2002

Vehicle routing problem is known to be a NP-hard problem, and is traditionally solved by some heuristic approaches. This paper investigates the application of the computer method COMSOAL to the optimal vehicle routing selection problem. The COMSOAL (Computer Method of Sequencing Operations for Assembly Lines) is a computer heuristic originally developed to solve an assembly line balancing problem a few decades ago. The solution methodology of repeatedly running COMSOAL will result in many feasible solutions from which the best is chosen. This solution approach now becomes viable thanks to the significantly increased speed of recent computer technology. This paper discusses the adaptation of the COMSOAL approach to the known set of simple vehicle routing example problem. The results show that the COMSOAL can be a good possible approach to solve the vehicle routing problem.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.144-151
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• 2005

We propose an optimization method to reduce the assembly time of chip mounters. Feeder arrangement and assembly sequence are determined considering the multiple feeder assignment. The problem is divided into two sub-problems: feeder arrangement problem and assembly sequence problem. We present mathematical model for each sub-problem. The clustering algorithm and assignment algorithm are applied to solve the feeder arrangement problem. The assignment algorithm and connection algorithm are applied to solve the assembly sequence problem. Simulation results are then presented to verity the usefulness of the proposed method.