• Management Science and Financial Engineering
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• v.20 no.1
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• pp.17-19
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• 2014

In this paper, we consider an interesting variant of the inverse minimum traveling salesman problem. Given an instance (G, w) of the minimum traveling salesman problem defined on a metric space, we fix a specified Hamiltonian cycle $HC_0$. The task is then to adjust the edge cost vector w to w' so that the new cost vector w' satisfies the triangle inequality condition and $HC_0$ can be returned by the minimum spanning tree algorithm in the TSP-instance defined with w'. The objective is to minimize the total deviation between the original and the new cost vectors with respect to the $L_1$-norm. We call this problem the inverse metric traveling salesman problem against the minimum spanning tree algorithm and show that it is closely related to the inverse metric spanning tree problem.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.2
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• pp.125-137
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• 1997

We are concerned with the packing policy determines the optimal packing of products with variable sizes to minimize the penalty costs for idle space and product spliting. Optimal packing problem is closely related to the optimal packet/record sizing problem in that randomly generated data stream with variable bytes are divided into a unit of packet/record for transmitting or storing. Assuming the product size and the production period are independently determined by renewal process, we can approximate the renewal process and formulate the optimization problem that minimize the expected packing cost for a production period. The problem is divided into two cases according to whether a product is allowed to split or not. Computational results for various distributions will be given to verify the approximation procedure and the resulting optimization problem.

• 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 ...

• Journal of the military operations research society of Korea
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• v.35 no.3
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• pp.47-59
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• 2009

A hybrid Genetic Algorithm is applied to military resource allocation problem. Since military uses many resources in order to maximize its ability, optimization technique has been widely used for analysing resource allocation problem. However, most of the military resource allocation problems are too complicate to solve through the traditional operations research solution tools. Recent innovation in computer technology from the academy makes it possible to apply heuristic approach such as Genetic Algorithm(GA), Simulated Annealing(SA) and Tabu Search(TS) to combinatorial problems which were not addressed by previous operations research tools. In this study, a hybrid Genetic Algorithm which reinforces GA by applying local search algorithm is introduced in order to address military optimization problem. The computational result of hybrid Genetic Algorithm on Missile Interceptor Allocation problem demonstrates its efficiency by comparing its result with that of a simple Genetic Algorithm.

• Journal of the Korean Institute of Navigation
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• v.23 no.4
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• pp.63-76
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• 1999

This paper aims at developing an integrated fleet management support system for industrial carriers who usually control the vessels of their own or on a time charter to minimize the cost of shipping their cargoes. The work is mainly concerned with the operational management problem of the fleet owned by a major oil company, a typical industrial carrier. The optimal fleet management problem for the major oil company can be divided into two phase problem. The front end corresponds to the production operation problem of the transportation of crude oil, the refinery operation, and the distribution of product oil to comply with the demand of the market. The back end is to tackle the fleet scheduling problem to meet the seaborne transportation demand derived from the front end. Relevant optimization models for each phase are proposed and described briefly. Then a user-friendly integrated fleet management support system is built based on the proposed optimization models for both ends under Windows environment. A case study reflecting the practices of fleet management problem for the major oil company is carried out by using the system.

• Computational Structural Engineering
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• v.5 no.1
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• pp.133-142
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• 1992

This paper presents a method to apply the linear goal programming, which has rarely been used to the structural opimization problem due to its unique formulation, to large scale nonlinear structural optimization. The method can be used as a multicriteria optimization tool since goal programming removes the difficulty in defining an objective function and constraints. The method uses the finite element analysis, linear goal programming techniques and successive linearization to obtain the solution for the nonlinear goal optimization problems. The general formulation of the structural optimization problem into a nonlinear goal programming form is presented. The successive linearization method for the nonlinear goal optimization problem is discussed. To demonstrate the validity of the method, as a design tool, the minimum weight structural optimization problems with stress constraints are solved for the cases of 10, 25 and 200 trusses and compared with the results of the other works.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.1-11
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• 2018

In various engineering fields, determining the appropriate parameter set is a cumbersome and difficult task when solving optimization problems. Despite the appropriate parameter setting through parameter sensitivity analysis, there are limits to evaluating whether the parameters are appropriate for all optimization problems. For this reason, kinds of a Self-adaptive Harmony searches have been developed to solve various engineering problems by the appropriate setting of algorithm's own parameters according to the problem. In this study, various types of Self-adaptive Harmony searches were investigated and the characteristics of optimization were categorized. Six algorithms with a differentiation of optimization process were applied and compared with not only the mathematical optimization problem, but also the engineering problem, which has been applied widely in the algorithm performance comparisons. The performance of each algorithm was compared, and the statistical performance indicators were used to evaluate the application results quantitatively.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.6
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• pp.683-690
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• 2007

Topology optimization techniques have been developed as a very efficient design tool and utilized for design engineering processes in many industrial sections during the past decade. And topology optimization has become the focus into structural optimization design up to now. Recently, thermally actuated compliant mechanisms have a wide range of applications. In this research, the thermo-elastic problem is a coupled problem which has to consider heat transfer analysis and structural analysis. Hence, the thermo-elastic problem has to deal with heat transfer material properties and structural material properties at the same time. The numerical examples are presented. From the results, it was shown that in terms of the displacement after optimization. Moreover, this paper compared thermo-system, elastic-system with thermo-elastic system and was shown a good result of topology optimization while thermo-elastic system was used.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.1
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• pp.21-30
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• 2021

Mid-course trajectory of the missiles equipped with seeker should be designed to detect target within FOV of seeker and to maximize the maneuverability at the point of transition to terminal guidance phase. Because the trajectory optimization problems are generally hard to obtain the analytic solutions due to its own nonlinearity with several constraints, the various numerical methods have been presented so far. In this paper, mid-course trajectory optimization problem for boost-glide missiles is calculated by using SOCP (Second-Order Cone Programming) which is one of convex optimization methods. At first, control variable augmentation scheme with a control constraint is suggested to reduce state variables of missile dynamics. And it is reformulated using a normalized time approach to cope with a free final time problem and boost time problem. Then, partial linearization and lossless convexification are used to convexify dynamic equation and control constraint, respectively. Finally, the results of the proposed method are compared with those of state-of-the-art nonlinear optimization method for verification.

• Structural Engineering and Mechanics
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• v.84 no.4
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• pp.489-502
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• 2022

This paper is aimed at developing an optimization-based Finite Element model updating approach for structural damage identification and quantification. A modal flexibility-based error function is introduced, which uses modal assurance criterion to formulate the updating problem as an optimization problem. Because of the inexplicit input/output relationship between the candidate solutions and the error function's output, a robust and efficient optimization algorithm should be employed to evaluate the solution domain and find the global extremum with high speed and accuracy. This paper proposes a new multi-stage Selective Particle Swarm Optimization (SPSO) algorithm to solve the optimization problem. The proposed multi-stage strategy not only fixes the premature convergence of the original Particle Swarm Optimization (PSO) algorithm, but also increases the speed of the search stage and reduces the corresponding computational costs, without changing or adding extra terms to the algorithm's formulation. Solving the introduced objective function with the proposed multi-stage SPSO leads to a smart feedback-wise and self-adjusting damage detection method, which can effectively assess the health of the structural systems. The performance and precision of the proposed method are verified and benchmarked against the original PSO and some of its most popular variants, including SPSO, DPSO, APSO, and MSPSO. For this purpose, two numerical examples of complex civil engineering structures under different damage patterns are studied. Comparative studies are also carried out to evaluate the performance of the proposed method in the presence of measurement errors. Moreover, the robustness and accuracy of the method are validated by assessing the health of a six-story shear-type building structure tested on a shake table. The obtained results introduced the proposed method as an effective and robust damage detection method even if the first few vibration modes are utilized to form the objective function.