• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.1
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• pp.1-24
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• 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 Korean Operations Research and Management Science Society
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• v.18 no.2
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• pp.105-130
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• 1993

This study deals with finding a schedule for the movement of a material handling device (elevator) in a manufacturing plant. Two different algorithm (Traveling Salesman Technique and Greedy Algorithm) are used in the scheduling of the elevators using a simulation technique to determine the proper method of scheduling the elevator movement. Based on the simulation analysis, we have found that the Greedy algorithm serves better than the algorithm based on Traveling Salesman technique for scheduling the movement of a material handling device in the manufacturing plant.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.1-8
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• 2002

A Branch-and-Cut algorithm is a branch-and-bound algorithm in which rutting planes are generated throughout the branch-and-bound tree. It is now one of the most widespread and successful methods for solving mixed integer programming problems. In this paper we presents efficient implementation techniques of branch-and-cut algorithm for miked integer programming problems.

• Management Science and Financial Engineering
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• v.13 no.1
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• pp.35-53
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• 2007

We consider a(worst-case) robust optimization version of the Economic Order Quantity(EOQ) model. Order setup costs and inventory carrying costs are assumed to have uncertainty in their values, and the uncertainty description of the two parameters is supposed to be given by an ellipsoidal representation. A genetic algorithm combined with Monte Carlo simulation is proposed to approximate the ellipsoidal representation. The objective function of the model under ellipsoidal uncertainty description is derived, and the resulting problem is solved by another genetic algorithm. Computational test results are presented to show the performance of the proposed method.

• Korean Management Science Review
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• v.24 no.2
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• pp.33-42
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• 2007

In this paper we consider an alternate m machine scheduling problem in which each job having at most two eligible machines should be assigned with the objective of makespan minimization. For this problem. we propose a $O(m2^m)$ time rounding algorithm with performance ratio at most 1.5. For a little general problem where each job can be processed in at most three machines, we prove that a polynomial time algorithm does not exist with performance ratio less than 1.5.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.260-267
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• 2017

Quantum-inspired Genetic Algorithm (QGA) is a probabilistic search optimization method combined quantum computation and genetic algorithm. In QGA, the chromosomes are encoded by qubits and are updated by quantum rotation gates, which can achieve a genetic search. Asset-based weapon target assignment (WTA) problem can be described as an optimization problem in which the defenders assign the weapons to hostile targets in order to maximize the value of a group of surviving assets threatened by the targets. It has already been proven that the WTA problem is NP-complete. In this study, we propose a QGA and a hybrid-QGA to solve an asset-based WTA problem. In the proposed QGA, a set of probabilistic superposition of qubits are coded and collapsed into a target number. Q-gate updating strategy is also used for search guidance. The hybrid-QGA is generated by incorporating both the random search capability of QGA and the evolution capability of genetic algorithm (GA). To observe the performance of each algorithm, we construct three synthetic WTA problems and check how each algorithm works on them. Simulation results show that all of the algorithm have good quality of solutions. Since the difference among mean resulting value is within 2%, we run the nonparametric pairwise Wilcoxon rank sum test for testing the equality of the means among the results. The Wilcoxon test reveals that GA has better quality than the others. In contrast, the simulation results indicate that hybrid-QGA and QGA is much faster than GA for the production of the same number of generations.

• The KIPS Transactions:PartB
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• v.12B no.1 s.97
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• pp.1-8
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• 2005

The importance of software is getting high in development of the digital device (digital camcoder, digital camera, mp3 player, ....). And because the sire of software becomes larger and complicated, the necessity of software configuration management (to solves a software crisis) is increased. The general software configuration management system shows lack of the property and features of software development environment for image processing algorithm due to its wide range to be covered. Image processing algorithm development environment has properties like repetitive analysis and simulation using visual programming environment where, beside support of elementary development functions. component(or library) can be combined and tested interactively. Moreover, the method to look fast and effectively for component having similar function is required. In this paper, we present the system which supports the software configuration management method for a simulation tool and the property in the visual programming environment. And we relate our system to real simulation tool so as to check its ability as the software configuration management system for image processing algorithm development environment.

• Korean Journal of Construction Engineering and Management
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• v.24 no.2
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• pp.70-78
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• 2023

Inclinometer has several problems of 1)difficulty installing inclinometer casing, 2) measuring 2D local lateral displacement of retaining wall, 3) measurement by manpower. To solve such problems, a 2D LiDAR sensor-based retaining wall displacement measurement system was developed in previous studies. The purpose of this study is to select a displacement analysis algorithm to be applied in the retaining wall displacement measurement system. As a result of the displacement analysis algorithm selection, the M3C2 (Multiple Model to Model Cloud Comparison) algorithm with a displacement estimation error of 2mm was selected as the displacement analysis algorithm. If the M3C2 algorithm is applied in the system and the reliability of the displacement analysis result is secured through several field experiments. Convenient management of the displacement for the retaining wall is possible in comparison with the current measurement management.

• Korean Management Science Review
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• v.20 no.1
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• pp.51-64
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• 2003

The JSSP (Job Shop Scheduling Problem) Is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on genetic algorithm to address JSSP. we design scheduling method based on SGA (Single Genetic Algorithm) and PGA (Parallel Genetic Algorithm). In the scheduling method, the representation, which encodes the job number, is made to be always feasible, initial population is generated through integrating representation and G&T algorithm, the new genetic operators and selection method are designed to better transmit the temporal relationships in the chromosome, and island model PGA are proposed. The scheduling method based on genetic algorithm are tested on five standard benchmark JSSPs. The results were compared with other proposed approaches. Compared to traditional genetic algorithm, the proposed approach yields significant improvement at a solution. The superior results indicate the successful Incorporation of generating method of initial population into the genetic operators.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.4
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• pp.11-20
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• 1998

This paper deals with a mathematical model and an algorithm for the problem of determining k most vital arcs in the shortest path problem. First, we propose a 0-1 integer programming model for finding k most vital arcs in shortest path problem given the ordered set of paths with cardinality q. Next, we also propose an algorithm for finding k most vital arcs ln the shortest path problem which uses the 0-1 Integer programming model and shortest path algorithm and maximum flow algorithms repeatedly Malik et al. proposed a non-polynomial algorithm to solve the problem, but their algorithm was contradicted by Bar-Noy et al. with a counter example to the algorithm in 1995. But using our algorithm. the exact solution can be found differently from the algorithm of Malik et al.