• IE interfaces
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• v.10 no.2
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• pp.1-13
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• 1997

The problem of the formation of machine-part cells in FMS is a very important issue at the planning and operating stages of FMS. This problem is inherently a combinatorial optimization problem, proven to be NP-complete(or, NP-hard). Among the several kinds of approaches which have been applied to solve the combinatorial optimization problems, the Simulated Annealing(SA) algorithm, a technique of random search type with a flexibility in generating alternatives, is a powerful problem solving tool. In this paper, the SA algorithm is used to solve machine cell-part family formation problems. The primary purpose of the study is to find the near-optimal solution of machine cell-part family formation problem, whare the product volume and number of operations are prespecified, that can minimize the total material handling cost caused by exceptional elements and intercell moves as much as possible. The results show that the SA algorithm is able to find a near-optimal solution for practical problems of the machine cell-part family formation.

• Korean Journal of Computational Design and Engineering
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• v.19 no.4
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• pp.410-422
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• 2014

Unlike other weapon systems, a naval vessel has unique characteristics in that the vessel itself is a naval unit. In limited space, compartments with various objectives and characteristics need to be arranged, so that vessel performance is maximized. This paper studied a compartment arrangement algorithm that considers activity relationships among compartments and survivability of a vessel. Based on the study, a compartment arrangement application is developed that can generate various layout alternatives swiftly. The application developed in this study aims at automating a two dimensional compartment layout problem. A combinatorial optimization is performed with the differential evolution algorithm to achieve the optimized layout.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.71-78
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• 2013

An efficient vehicle routing heuristic for different vehicle moving times for forward and backward between two points is studied in this research. Symmetric distance or moving times are assumed to move back and forth between two points in general, but it is not true in reality. Also, various moving speeds along time zones are considered such as the moving time differences between rush hours or not busy daytimes. To solve this type of extremely complicated combinatorial optimization problems, delivery zones are specified and delivery orders are determined for promising results on the first stage. Then delivery orders in each zone are determined to be connected with other zones for a tentative complete delivery route. Improvement steps are followed to get an effective delivery route for unsymmetric-time-varing vehicle moving speed problems. Performance evaluations are done to show the effectiveness of the suggested heuristic using computer programs specially designed and developed using C++.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.234-240
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• 1999

This paper presents a new approach for large-scale generator maintenance scheduling optimizations. The generator preventive maintenance scheduling problems are typical discrete dynamic n-dimensional vector optimization ones with several inequality constraints. The considered objective function to be minimized a subset of{{{{ { R}^{n } }}}} space is the variance (i.g., second-order momentum) of operating reserve margin to levelize risk or reliability during a year. By its nature of the objective function, the optimal solution can only be obtained by enumerating all combinatorial states of each variable, a task which leads to computational explosion in real-world maintenance scheduling problems. This paper proposes a new priority search mechanism based on each generator's discrete sensitivity value which was analytically developed in this study. Unlike the conventional capacity-based priority search, it can prevent the local optimal trap to some extents since it changes dynamically the search tree in each iteration. The proposed method have been applied to two test systems (i.g., one is a sample system with 10 generators and the other is a real-world lage scale power system with 280 generators), and the results anre compared with those of the conventional capacith-based search method and combinatorial optimization method to show the efficiency and effectiveness of the algorithm.

• Journal of Mechanical Science and Technology
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• v.19 no.5
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• pp.1072-1079
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• 2005

Conceptual design is the first step in the overall process of product design. Its intrinsic uncertainty, imprecision, and lack of information lead to the fact that current conceptual design activities in engineering have not been computerized and very few CAD systems are available to support conceptual design. In most of the current intelligent design systems, approach of principle synthesis, such as morphology matrix, bond graphic, or design catalogues, is usually adopted to deal with the concept generation, in which optional concepts are generally combined and enumerated through function analysis. However, as a large number of concepts are generated, it is difficult to evaluate and optimize these design candidates using regular algorithm. It is necessary to develop a new approach or a tool to solve the concept generation. Generally speaking, concept generation is a problem of concept synthesis. In substance, this process of developing design candidate is a combinatorial optimization process, viz., the process of concept generation can be regarded as a solution for a state-place composed of multi-concepts. In this paper, genetic algorithm is utilized as a feasible tool to solve the problem of combinatorial optimization in concept generation, in which the encoding method of morphology matrix based on function analysis is applied, and a sequence of optimal concepts are generated through the search and iterative process which is controlled by genetic operators, including selection, crossover, mutation, and reproduction in GA. Several crucial problems on GA are discussed in this paper, such as the calculation of fitness value and the criteria for heredity termination, which have a heavy effect on selection of better concepts. The feasibility and intellectualization of the proposed approach are demonstrated with an engineering case. In this work concept generation is implemented using GA, which can facilitate not only generating several better concepts, but also selecting the best concept. Thus optimal concepts can be conveniently developed and design efficiency can be greatly improved.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.124-126
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• 2002

The general capacitor placement problem is a combinatorial optimization problem having an objective function composed of power losses and capacitor installation costs subject to bus voltage constraints. In this paper, the method employing the chaos search algorithm is proposed to solve optimal capacitor placement problem with reducing computational effort and enhancing optimality of the solution. Chaos method in optimization problem searches the global optimal solution on the regularity of chaotic motions and easily escapes from local or near optimal solution than stochastic optimization algorithms. The chaos optimization method is tested on 9 buses and 69 buses system to illustrate the effectiveness of the proposed method.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.519-524
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• 1996

This paper describes the application of Genetic Algorithms(GAs) to nonlinear constrained mixed optimization problems. Genetic Algorithms are combinatorial in nature, and therefore are computationally suitable for treating discrete and integer design variables. But, several problems that conventional GAs are ill defined are applicaiton of penalty function that can be adapted to transform a constrained optimization problem into an unconstrained optimization problem into an unconstrained one and premature convergence of solution. Thus, we developed an modified GAs to solve this problems, and two examples are given to demonstrate the effectiveness of the methodology developed in this paper.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1169-1182
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• 2020

The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACO_R, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.623-625
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• 1998

This paper focuses on the fast convergence in nonlinear parameter optimization which is necessary for the fitting of nonlinear models to data. The simulated annealing(SA) and genetic algorithm(GA), which are widely used for combinatorial optimization problems, are stochastic strategy for search of the ground state and a powerful tool for optimization. However, their main disadvantage is the long convergence time by unnecessary extra works. It is also recognised that gradient-based nonlinear programing techniques would typically fail to find global minimum. Therefore, this paper develops a modified SA which is the SDS(Stochastic deterministic stochastic) algorithm can minimize cost function of optimal problem.

• Korea Real Estate Review
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• v.28 no.1
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• pp.23-37
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• 2018

The genetic algorithm (GA) and branch and bound (B&B) methods are the useful methods of searching the optimal project combination (combinatorial optimization) to maximize the project effect considering the budget constraint and the balance of regional development with regard to the Urban Regeneration New Deal policy, the core real estate policy of the Moon Jae-in government. The Ministry of Land, Infrastructure, and Transport (MOLIT) will choose 13 central-city-area-type projects, 2 economic-base-type projects, and 10 public-company-proposal-type projects among the numerous projects from 16 local governments while each government can apply only 4 projects, respectively, for the 2017 Urban Regeneration New Deal project. If MOLIT selects only those projects with a project effect maximization purpose, there will be unselected regions, which will harm the balance of regional development. For this reason, an optimization model is proposed herein, and a combinatorial optimization method using the GA and B&B methods should be sought to satisfy the various constraints with the object function. Going forward, it is expected that both these methods will present rational decision-making criteria if the central government allocates a special-purpose-limited budget to many local governments.