• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.195-204
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• 2003

Network optimization is being increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. Networks provide a useful way to modeling real world problems and are extensively used in practice. Many real world applications impose on more complex issues, such as, complex structure, complex constraints, and multiple objects to be handled simultaneously and make the problem intractable to the traditional approaches. Recent advances in evolutionary computation have made it possible to solve such practical network optimization problems. The invited talk introduces a thorough treatment of evolutionary approaches, i.e., hybrid genetic algorithms approach to network optimization problems, such as, fixed charge transportation problem, minimum cost and maximum flow problem, minimum spanning tree problem, multiple project scheduling problems, scheduling problem in FMS.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2007년도 추계학술대회 및 정기총회
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• pp.27-37
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• 2007

Many problems related to airline business belong to large-scale optimization problems, so that it is expected that the state-of-art optimization techniques are widely applied to making the airline operation effective and competitive. This paper introduces the concepts and mathematical models of various optimization problems in airline system. Airlines involve many activities that utilize airline resources such as aircrafts and crews to make profit. We view the airline activities in the planning and operational aspects. In the planning viewpoint, we discuss the flight schedule design problem that impacts on passenger demand directly. For aircraft and crews, we deal with fleet assignment, aircraft routing, crew pairing optimization, and crew assignment problem. In the operational viewpoint, we concern schedule recovery problems for aircrafts and crew using the method of reassigning available resources when airlines face with the unexpected situations.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
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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.

• 경영과학
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• 제17권1호
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• pp.55-64
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• 2000

By managing not only geographical information but also various kinds of attribute data. GIS presents useful information for decision-makings. Most of decision-making problems using GIS can be formulated into network-optimization problems. In this study we deal with the implementation of network optimization system that extracts data from the database in GIS. solves a network optimization problem and present optimal solutions through GIS' graphical user interface. We design a nitwork optimization system and present some implementation techniques by showing a prototype of the network optimization system. Our network optimization system consists of three components : the interface module for user and GIS the basic network the program module the advanced network optimization program module. To handle large-scale networks the program module including various techniques for large sparse networks is also considered, For the implementation of the network optimization system we consider two approaches : the method using script languages supported by GIS and the method using client tools of GIS. Finally some execution results displayed by the prototype version of network optimization system are given.

• 한국지능시스템학회논문지
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• 제19권3호
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• pp.432-436
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• 2009

We have developed a new optimization algorithm termed simple bacteria cooperative optimization (sBCO) based on bacteria behavior patterns [1]. In [1], we have introduced the algorithm with basic operations and showed its feasibility with some function optimization problems. Since the sBCO was the first version with only basic operations, its performance was not so good. In this paper, we adopt a new operation, rank replacement, to the sBCO for improving its performance and compare its results to those of the simple genetic algorithm (sGA) which has been well known and widely used as an optimization algorithm. It was found from the experiments with four function optimization problems that the sBCO with rank replacement was superior to the sGA. This shows that our algorithm can be a good optimization algorithm.

• 대한기계학회논문집
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• 제15권2호
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• pp.544-556
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• 1991

The optimization of many engineering design problems requires a nonlinear programming algorithm that is robust and efficient. A general-purpose nonlinear optimization program IDOL (Interactive Design Optimization Library) is developed based on the Augmented Lagrange Mulitiplier (ALM) method. The ideas of selecting a good initial design point, using resonable initial values for Lagrange multipliers, constraints scaling, descent vector restarting, and dynamic stopping criterion are employed for computational enhancement to the ALM method. A descent vector is determined by using the Broydon-Fletcher-Goldfarb-Shanno (BFGS) method. For line search, the Incremental-Search method is first used to find bounds on the solution, then the bounds are reduced by the Golden Section method, and finally a cubic polynomial approximation technique is applied to locate the next design point. Seven typical test problems are solved to show IDOL efficient and robust.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회A
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• pp.360-365
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• 2007

This work is concerned with the topology optimization of three-dimensional cooling fins or heat sinks. Motivated by earlier success of the Internal Element Connectivity Method (I-ECP) method in two-dimensional problems, the extension of I-ECP to three-dimensional problems is carried out. The main efforts were made to maintain the numerical trouble-free characteristics of I-ECP for full three-dimensional problems; a serious numerical problem appearing in thermal topology optimization is erroneous temperature undershooting. The effectiveness of the present implementation was checked through the design optimization of three-dimensional fins.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2004년도 가을 학술발표회 논문집
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• pp.411-418
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• 2004

Using a level set method we develop a shape optimization method applied to energy flow problems in steady state. The boundaries are implicitly represented by the level set function obtainable from the 'Hamilton-Jacobi type' equation with the 'Up-wind scheme.' The developed method defines a Lagrangian function for the constrained optimization. It minimizes a generalized compliance, satisfying the constraint of allowable volume through the variations of implicit boundary. During the optimization, the boundary velocity to integrate the Hamilton-Jacobi equation is obtained from the optimality condition for the Lagrangian function. Compared with the established topology optimization method, the developed one has no numerical instability such as checkerboard problems and easy representation of topological shape variations.

• 대한수학회지
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• 제47권1호
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• pp.17-28
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• 2010

In this paper we deal with linear chance-constrained optimization problems, a class of problems which naturally arise in practical applications in finance, engineering, transportation and scheduling, where decisions are made in presence of uncertainty. After giving the deterministic equivalent formulation of a linear chance-constrained optimization problem we construct a conjugate dual problem to it. Then we provide for this primal-dual pair weak sufficient conditions which ensure strong duality. In this way we generalize some results recently given in the literature. We also apply the general duality scheme to a portfolio optimization problem, a fact that allows us to derive necessary and sufficient optimality conditions for it.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2002년도 가을 학술발표회 논문집
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• pp.203-210
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• 2002

The great advantages on the Genetic Algorithms(GAs) are ease of implementation, and robustness in solving a wide variety of problems, several GAs based optimization models for solving complex structural problems were proposed. However, there are two major disadvantages in GAs. The first disadvantage, implementation of GAs-based optimization is computationally too expensive for practical use in the field of structural optimization, particularly for large-scale problems. The second problem is too difficult to find proper parameter for particular problem. Therefore, in this paper, a Distributed Hybrid Genetic Algorithms(DHGAs) is developed for structural optimization on a cluster of personal computers. The algorithm is applied to the minimum weight design of steel structures.