• Journal of Korean Society of Transportation
• /
• v.25 no.6
• /
• pp.161-170
• /
• 2007

The purpose of this study is to find the solution that overcomes the existing assumption of symmetric cost functions in multi-class assignment. In the assignment problem, the assumption of a symmetric cost function means that the link cost is determined by each unique mode and is not affected by any other modes. In this study, the authors have applied a diagonalized algorithm and a heuristic model based on column generation to a multi-class assignment model and analyzed the result. Through the study, the authors found that the diagonalized algorithm produces equilibrium solutions by the initial convergence condition. In contrast to the diagonalized algorithm, the column generation algorithm has improved the solution model to overcome the problem of equilibrium solutions in the diagonalized algorithm.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.1 no.2
• /
• pp.69-82
• /
• 1995

This study is concerned with developing a heuristic algorithm for solving a class of ninlinear integer programs(NLIP). Exact algrithm for solving a NLIP either may not exist, or may take an unrealistically large amount of computing time. This study develops a new heuristic, the Excursion Algorithm(EA), for solving a class of NLIP's. It turns out that excursions over a bounded feasible and/or infeasible region is effective in alleviation the risks of being trapped at a lical optimum. The developed EA is applied to the redundancy optimization problems for improving the system safety, and is compared with other existing heuristic methods. We also include simulated annealing(SA) method in the comparision experiment due to ist populatrity for solving complex combinatorial problems. Computational results indicate that the proposed EA performs consistently better than the other in terms of solution quality, with moderate increase in computing time. Therefore, the proposed EA is believed to be an attractive alternative to other heuristic methods.

• Korean Journal of Computational Design and Engineering
• /
• v.15 no.1
• /
• pp.1-10
• /
• 2010

Recently, a shipyard is making every effort to efficiently manage equipments of resources such as a gantry crane, transporter, and so on. So far block lifting scheduling of a gantry crane has been manually performed by a manager of the shipyard, and thus it took much time to get scheduling results and moreover the quality of them was not optimal. To improve this, a block lifting scheduling system of the gantry crane using optimization techniques was developed in this study. First, a block lifting scheduling problem was mathematically formulated as a multi-objective optimization problem, considering the minimization of travel distance at an idle state and wire replacement during block lifting. Then, to solve the problem, a meta-heuristic optimization algorithm based on the genetic algorithm was proposed. To evaluate the efficiency and applicability of the developed system, it was applied to an actual block lifting scheduling problem of the shipyard. The result shows that blocks can be efficiently lifted by the gantry crane using the developed system, compared to manual scheduling by a manager.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.3
• /
• pp.943-952
• /
• 2000

The surface reconstruction problem from a set of wire-frame contours is very important in diverse fields such as medical imaging or computer animation. In this paper, surface triangulation method is proposed for solving the problem. Generally, many optimal triangulation techniques suffer from the large computation time but heuristic approaches may produce very unnatural surface when contours are widely different in shape. To compensate the disadvantages of these approaches, we propose a new heuristic triangulation method which iteratively decomposes the surface generation problem from a band (a pair of vertices chain) into tow subproblems from two sub-bands. Generally, conventional greedy heuristic contour triangulation algorithm, suffer from the drastic error propagation during surface modeling when the adjacent contours are different in shape. Our divide-and-conquer algorithm, called band partitioning algorithm, processes eccentric parts of the contours first with more global information. Consequently, the resulting facet model becomes more stable and natural even though the shapes are widely different. An interesting property of our method is hat it supports multi-resolution capability in surface modeling time. According to experiments, it is proved to be very robust and efficient in many applications.

• Journal of Computing Science and Engineering
• /
• v.8 no.4
• /
• pp.199-206
• /
• 2014

The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.11 no.18
• /
• pp.7-15
• /
• 1988

This study is concerned with selecting mutually dependent quality improvement alternatives with resource constraints. These qualify improvement alternatives art different fro the tradition at alternatives which are independent from each other. In other words, selection of any improvement alternative requires other related specific improvement. Also the overall product quality in a multi stage manufacturing process is characterized by a complex multiplication method rather than a simple addition method which dose not allow to solve a linear knapsack problem despite its popularity in the traditional study. This study suggests a non-linear integer programming model for selecting mutually dependent quality improvement alternatives in multi-stage manufacturing process. In order to apply the model to selecting alternatives. This study also suggests a heuristic mode1 based on a dynamic programming model which is more practical than the non-linear integer programming model. The logic of the heuristic model enables 1) to estimate improvement effectiveness values on all improvement alternatives specifically defined for this study. 2) to arrange the effectiveness values in a descending order, and 3) to select the best one among the alternatives based on their forward and backward linkage relationships. This process repeats to selects other best alternatives within the resource constraints. This process is presented in a Computer programming in Appendix A. Alsc a numerical example of model application is presented in Chapter 4.

• Industrial Engineering and Management Systems
• /
• v.13 no.2
• /
• pp.163-171
• /
• 2014

Under amazing increase in markets and certain demand on qualified service in the delivery system, global logistic optimization is becoming a keen interest to provide an essential infrastructure coping with modern competitive prospects. As a key technology for such deployment, we have been engaged in the practical studies on vehicle routing problem (VRP) in terms of Weber model, and developed a hybrid approach of meta-heuristic methods and the graph algorithm of minimum cost flow problem. This paper extends such idea to multi-depot VRP so that we can give a more general framework available for various real world applications including those in green or low carbon logistics. We show the developed procedure can handle various types of problem, i.e., delivery, direct pickup, and drop by pickup problems in a common framework. Numerical experiments have been carried out to validate the effectiveness of the proposed method. Moreover, to enhance usability of the method, Google Maps API is applied to retrieve real distance data and visualize the numerical result on the map.

• Advances in Energy Research
• /
• v.4 no.4
• /
• pp.255-274
• /
• 2016

This paper presents a novel symbiotic organisms search (SOS) algorithm to optimize both real power loss (RPL) and voltage stability limit (VSL) of a transmission network by controlling the variables such as unified power flow controller (UPFC) location, UPFC series injected voltage magnitude and phase angle and transformer taps simultaneously. Mathematically, this issue can be formulated as nonlinear equality and inequality constrained multi objective, multi variable optimization problem with a fitness function integrating both RPL and VSL. The symbiotic organisms search (SOS) algorithm is a nature inspired optimization method based on the biological interactions between the organisms in ecosystem. The advantage of SOS algorithm is that it requires a few control parameters compared to other meta-heuristic algorithms. The proposed SOS algorithm is applied for solving optimum control variables for both single objective and multi-objective optimization problems and tested on New England 39 bus test system. In the single objective optimization problem only RPL minimization is considered. The simulation results of the proposed algorithm have been compared with the results of the algorithms like interior point successive linear programming (IPSLP) and bacteria foraging algorithm (BFA) reported in the literature. The comparison results confirm the efficacy and superiority of the proposed method in optimizing both single and multi objective problems.

• Journal of Korean Society for Quality Management
• /
• v.17 no.1
• /
• pp.35-47
• /
• 1989

This paper is concerned with a development and evaluation of heuristic algorithms for the n-job, M-stage flowshop with sequence dependent setup times. Three heuristic algorithms, CAIDAN, DANNEN and PETROV, are proposed. The makespan is taken as a performance measure for the algorithms. The experiment for each algorithm is designed for a $4{\times}3{\times}3$ factorial design with 360 observations. The experimental factors are PS (ratio of processing times to setup times), M (number of machines), and N (number of jobs). The makespan of the proposed heuristic algorithms is compared with the optimal makespan obtained by the complete enumeration method. The result of comparision of performance measure is called a relative error. The mean relative errors of CAIDAN, DANNEN and PETROV algorithms are 4.488%. 6.712% and 7.282%, respectively. The computational results are analysed using SPSS. The experimental results show that the three factors are statistically signiticant at 5% level.

• Structural Engineering and Mechanics
• /
• v.63 no.6
• /
• pp.825-835
• /
• 2017

Significant improvements to methodologies on structural damage detection (SDD) have emerged in recent years. However, many methods are related to inversion computation which is prone to be ill-posed or ill-conditioning, leading to low-computing efficiency or inaccurate results. To explore a more accurate solution with satisfactory efficiency, a PSO-INM algorithm, combining particle swarm optimization (PSO) algorithm and an improved Nelder-Mead method (INM), is proposed to solve multi-sample objective function defined based on Bayesian inference in this study. The PSO-based algorithm, as a heuristic algorithm, is reliable to explore solution to SDD problem converted into a constrained optimization problem in mathematics. And the multi-sample objective function provides a stable pattern under different level of noise. Advantages of multi-sample objective function and its superior over traditional objective function are studied. Numerical simulation results of a two-storey frame structure show that the proposed method is sensitive to multi-damage cases. For further confirming accuracy of the proposed method, the ASCE 4-storey benchmark frame structure subjected to single and multiple damage cases is employed. Different kinds of modal identification methods are utilized to extract structural modal data from noise-contaminating acceleration responses. The illustrated results show that the proposed method is efficient to exact locations and extents of induced damages in structures.