• Journal of the Korea Society of Computer and Information
• /
• v.20 no.10
• /
• pp.61-68
• /
• 2015

This paper deals with the pre-marshalling problem that the containers of container yard at container terminal are relocated in consensus sequence of loading schedule of container vessel. This problem is essential to improvement of competitive power of terminal. This problem has to relocate the all of containers in a bay with minimum number of movement. There are various algorithms such as metaheuristic as genetic algorithm and heuristic algorithm in order to find the solution of this problem. Nevertheless, there is no unique general algorithm that is suitable for various many data. And the main drawback of metaheuristic methods are not the solution finding rule but can be find the approximated solution with many random trials and by coincidence. This paper can be obtain the solution with O(m) time complexity that this problem deals with bin packing problem for m stack bins with descending order of take out ranking. For various experimental data, the proposed algorithm can be obtain the optimal solutions for all of data. And to conclude, this algorithm can be show that most simple and general algorithm with simple optimal solution finding rule.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.22 no.1
• /
• pp.1-24
• /
• 1997

In this paper, we have proposed new solution methods to solve TSLP (two-stage stochastic linear programming) problems. One solution method is to combine the analytic center concept with Benders' decomposition strategy to solve TSLP problems. Another method is to apply an idea proposed by Geoffrion and Graves to modify the L-shaped algorithm and the analytic center algorithm. We have compared the numerical performance of the proposed algorithms to that of the existing algorithm, the L-shaped algorithm. To effectively compare those algorithms, we have had computational experiments for seven test problems.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.10
• /
• pp.77-83
• /
• 2015

This paper suggests heuristic algorithm to obtain optimal solution of minimum number of delay aircraft in airport arrivals/departures problem. This problem can be solved only mathematical optimization method. The proposed algorithm selects the minimum delays capacity in various airport capacities for number of arrivals/departures aircraft in $i^{th}$ time interval (15 minutes). In details, we apply median selection method and left-right selection method. This algorithm can be get the optimal solution of minimum number of delay aircraft for sixes actual experimental data.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.4
• /
• pp.1415-1421
• /
• 2015

This paper presents a new algorithm called Quantum-behaved Electromagnetism-like Mechanism Algorithm which is used to solve economic load dispatch of power system. Electromagnetism-like mechanism algorithm simulates attraction and repulsion mechanism for particles in the electromagnetic field. Every solution is a charged particle, and it move to optimum solution according to certain criteria. Quantum-behaved electromagnetism-like mechanism algorithm merges quantum computing theory with electromagnetism-like mechanism algorithm. Superposition characteristic of quantum methodology can make a single particle present several states, and the characteristic potentially increases population diversity. Probability representation of quantum methodology is to make particle state be presented according to a certain probability. And the quantum rotation gates are used to realize update operation of particles. The algorithm is tested for 13-generator system and 40-generator system, which validates it can effectively solve economic load dispatch problem. Through performance comparison, it is obvious the solution is superior to other optimization algorithm.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.23 no.1
• /
• pp.1-16
• /
• 1998

The zone clustering problem arising from several area such as deciding the optimal location of ambient measuring stations is to devide the 2-dimensional area into several sub areas in which included individual zone shows simimlar properties. In general, the optimal solution of this problem is very hard to obtain. Therefore, instead of finding an optimal solution, the generation of near optimal solution within the limited time is more meaningful. In this study, the combination of a genetic algorithm and the modified k-means method is used to obtain the near optimal solution. To exploit the genetic algorithm effectively, a representation of chromsomes and appropriate genetic operators are proposed. The k-means method which is originally devised to solve the object clustering problem is modified to improve the solutions obtained from the genetic algorithm. The experiment shows that the proposed method generates the near optimal solution efficiently.

• Proceedings of the KIEE Conference
• /
• 1999.07c
• /
• pp.1506-1509
• /
• 1999

This paper presents a new economic dispatch algorithm to improve the unit commitment solution while guaranteeing the near optimal solution without reducing calculation speed. The conventional economic dispatch algorithms have the problem that it is not applicable to the unit commitment formulation due to the frequent on/off state changes of units during the unit commitment calculation. Therefore, piecewise linear iterative method have generally been used for economic dispatch algorithm for unit commitment. In that method, the approximation of the generator cost function makes it hard to obtain the optimal economic dispatch solution. In this case, the solution can be improved by introducing a inverse of the incremental cost function. The proposed method is tested with sample system. The results are compared with the conventional piecewise linear iterative method. It is shown that the proposed algorithm yields more accurate and economical solution without calculation speed reduction.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.5
• /
• pp.31-39
• /
• 2015

The degree-constrained minimum spanning tree (d-MST) problem is considered NP-complete for no exact solution-yielding polynomial algorithm has been proposed to. One thus has to resort to an heuristic approximate algorithm to obtain an optimal solution to this problem. This paper therefore presents a polynomial time algorithm which obtains an intial solution to the d-MST with the help of Kruskal's algorithm and performs k-opt on the initial solution obtained so as to derive the final optimal solution. When tested on 4 graphs, the algorithm has successfully obtained the optimal solutions.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.31 no.2
• /
• pp.69-85
• /
• 2006

Transductive Support Vector Machine(TSVM) is one of semi-supervised learning algorithms which exploit the domain structure of the whole data by considering labeled and unlabeled data together. Although it was proposed several years ago, there has been no efficient algorithm which can handle problems with more than hundreds of training examples. In this paper, we propose an efficient branch-and-bound algorithm which can solve large-scale TSVM problems with thousands of training examples. The proposed algorithm uses two bounding techniques: min-cut bound and reduced SVM bound. The min-cut bound is derived from a capacitated graph whose cuts represent a lower bound to the optimal objective function value of the dual problem. The reduced SVM bound is obtained by constructing the SVM problem with only labeled data. Experimental results show that the accuracy rate of TSVM can be significantly improved by learning from the optimal solution of TSVM, rather than an approximated solution.

• Journal of the Korean Institute of Telematics and Electronics A
• /
• v.31A no.12
• /
• pp.142-151
• /
• 1994

This paper proposes new algorithms to solve canonical piecewise-linear equations with linear partitions and illustrates their efficiency through the analysis of resistive network. The basic idea of the proposed algorithm is to find the best next guess, closest to the actual solution, at each Newton-Raphson (N-R) iteration by comparing the images of nest guess candidates and that of the actual solution. The proposed algorithm can reduce the number of the N-R iterations rquired for convergence greatly, compared to the actual solution, at each Newton-Raphson (N-R) iteration by comparing the images of next guess candidates and that of the actual solution. The proposed algorithm can reduce the number of the N-R iterations required for convergence greatly, compared to the Katzenelson algorithm. When applied to analyzing test circuits, the proposed algorithm required 8 to 20 times fewer N-R iterations and 5 to 10 times less CPU time than the Katzenelson algorithm, depending on the size of the circuits. The experimental results also exhibit that the efficiency of the proposed algorithm over the Katzenelson algorithm increases as the number of the piecewise-linear regions for the representation of the circuit.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.29 no.10 s.241
• /
• pp.1369-1376
• /
• 2005

In structural design, the design variables are frequently selected from certain discrete values. Various optimization algorithms have been developed fDr discrete design. It is well known that many function evaluations are needed in such optimization. Recently, sequential algorithm with orthogonal arrays (SOA), which is a search algorithm for a local minimum in a discrete space, has been developed. It considerably reduces the number of function evaluations. However, it only finds a local minimum and the final solution depends on the initial values of the design variables. A new algorithm is proposed to adopt a genetic algorithm (GA) in SOA. The GA can find a solution in a global sense. The solution from the GA is used as the initial design of SOA. A sequential usage of the GA and SOA is carried out in an iterative manner until the convergence criteria are satisfied. The performance of the algorithm is evaluated by various examples.