• 제목/요약/키워드: Goal Programming Method

검색결과 110건 처리시간 0.023초

Hierarchical Web Structuring Using Integer Programming

  • 이우기;김승;김한도;강석호
    • 한국경영과학회:학술대회논문집
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    • 한국경영과학회 2004년도 추계학술대회 및 정기총회
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    • pp.51-67
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    • 2004
  • World Wide Web is nearly ubiquitous and the tremendous growing number of Web information strongly requires a structuring framework by which an overview visualization of Web sites has provided as a visual surrogate for the users. We have a viewpoint that the Web site is a directed graph with nodes and arcs where the nodes correspond to Web pages and the arcs correspond to hypertext links between the Web pages. In dealing with the WWW, the goal in this paper is not to derive a naive shortest path or a fast access method, but to generate an optimal structure based on the context centric weight. We modeled a Web site formally so that a integer programming model can be formulated. Even if changes such as modification of the query terms, the optimized Web site structure can be maintained in terms of sensitivity.

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e-Business기업의 핵심역량 집중화전의에 관한 연구 - FGP를 이용한 접근법 - (A Study on the Concentration Strategy of an E-Business Firm to its Core Competence - Approach by the Fuzzy Goal Programming -)

  • 황봉기;김종순
    • 산학경영연구
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    • 제15권
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    • pp.99-114
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    • 2002
  • 최근에 e-Business에 관한 여러 가지 사업모델이 발표되고 있다. 그러나 각자의 사업환경이 상이하기 때문에 각각 주어진 여건에 적합한 사업모델이 요구된다. 즉, 자본의 규모, 기술력, 정보수집능력과 정보량, 이익이나 목표고객 등의 여건을 고려한 e-Business모델을 개발할 필요성이 있다. e-Business 기업의 가치를 창출하는 방법은 여러 가지가 있을 수 있다. 그 중에서도 기업의 핵심역량을 강화하여 일부분만 성장 발전시키는 방법이 있을 수 있다. 이는 기업의 제한된 제약조건하에 상대적 경쟁우위에 있는 핵심역량부분에 차별적으로 집중하기 위한 적정 생산 투자수준을 찾아내는 문제로 전환하여 투자의 우선 순위를 모색할 수도 있을 것이다. 본 연구는 불명확한 정보를 이용하여 의사결정을 할 때 효과적으로 투자 우선 순위를 결정하기 위한 방법을 제안하고 있다. 그 방법으로써 FGP(fuzzy goal programming)문제에 대한 허용공차를 최소화하는 모형을 제시한다.

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차량용 가스스프링의 최적설계에 관한 연구 (A Study on the Optimal Design of Automotive Gas Spring)

  • 이춘태
    • 드라이브 ㆍ 컨트롤
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    • 제14권4호
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    • pp.45-50
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    • 2017
  • The gas spring is a hydropneumatic adjusting element, consisting of a pressure tube, a piston rod, a piston and a connection fitting. The gas spring is filled with compressed nitrogen within the cylinder. The filling pressure acts on both sides of the piston and because of area difference it produces an extension force. Therefore, a gas spring is similar in function compare to mechanical coil spring. Conversely, optimization is a process of finding the best set of parameters to reach a goal while not violating certain constraints. The AMESim software provides NLPQL (Nonlinear Programming by Quadratic Lagrangian) and GA (genetic algorithm) for optimization. The NLPQL method builds a quadratic approximation to the Lagrange function and linear approximations to all output constraints at each iteration, starting with the identity matrix for the Hessian of the Lagrangian, and gradually updating it using the BFGS method. On each iteration, a quadratic programming problem is solved to find an improved design until the final convergence to the optimum design. In this study, we conducted optimization design of the gas spring reaction force with NLPQL.

시뮬레이션 최적화 기법과 절삭공정에의 응용 (Simulation Optimization Methods with Application to Machining Process)

  • 양병희
    • 한국시뮬레이션학회논문지
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    • 제3권2호
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    • pp.57-67
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    • 1994
  • For many practical and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computer simulation is one of the most effective means of studying such complex systems. In this paper, with discussion of simulation optimization techniques, a case study in machining process for application of simulation optimization is presented. Most of optimization techniques can be classified as single-or multiple-response techniques. The optimization of single-response category, these strategies are gradient based search methods, stochastic approximate method, response surface method, and heuristic search methods. In the multiple-response category, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphical method, direct search method, constrained optimization, unconstrained optimization, and goal programming methods. The choice of the procedure to employ in simulation optimization depends on the analyst and the problem to be solved.

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적층순서 최적화 알고리듬의 평가;유전 알고리듬과 분기법 (A Comparison of Stacking Sequence Optimization Schemes;Genetic Algorithm and Branch and Bound Method)

  • 김태욱;신정우
    • 대한기계학회:학술대회논문집
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    • 대한기계학회 2003년도 춘계학술대회
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    • pp.420-424
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    • 2003
  • Stacking sequence optimization needs discrete programming techniques because ply angles are limited to a fixed set of angles such as $0^{\circ},\;{\pm}45^{\circ},\;90^{\circ}$. Two typical methods are genetic algorithm and branch and bound method. The goal of this paper is to compare the methods in the light of their efficiency and performance in handling the constraints and finding the global optimum. For numerical examples, maximization of buckling load is used as objective and optimization results from each method are compared.

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Generation of OC and MMA topology optimizer by using accelerating design variables

  • Lee, Dongkyu;Nguyen, Hong Chan;Shin, Soomi
    • Structural Engineering and Mechanics
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    • 제55권5호
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    • pp.901-911
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    • 2015
  • The goal of this study is to investigate computational convergence of optimal solutions, with respect to optimality criteria (OC) method and methods of moving asymptotes (MMA) as optimization model for non-linear programming of material topology optimization using an acceleration method that makes design variables rapidly move toward almost 0 and 1 values. 99 line topology optimization MATLAB code uses loop vectorization and memory pre-allocation as properly exploiting the strengths of MATLAB and moves portions of code out of the optimization loop so that they are only executed once as restructuring the program. Numerical examples of a simple beam under a lateral load and a given material density limitation provide merits and demerits of the present OC and MMA for 99 line topology optimization code of continuous material topology optimization design.

퍼지 DHP를 이용한 정보시스템 프로젝트의 선정 (The Application of Fuzzy DHP in MIS Project Selection)

  • 정희진;이승인
    • 한국컴퓨터정보학회논문지
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    • 제3권2호
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    • pp.189-199
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    • 1998
  • 본 논문에서는 성공적인 기업활동에 많은 영향을 미치는 정보시스템 계획에 있어주요 관리활동중의 하나인 정보시스템 프로젝트의 선정과정에 대해 DHP(Delphic Hierarchy Process)기법과 FZOGP(Fuzzified Zero-One Goal Programming) 모형을 검토하였다. 정보시스템 선정과정에 적용되는 기존의 AHP에서는 우선순위 결정에 있어 객관적인 측면과 전문가들의 의견이 충분히 반영되지 못하기 때문에 Delphi법을 동시에 고려하는 DHP기법이 필요성이 제시되어지는 것이다. 그러나 이러한 우선순위결정기법은 의사결정과정에 있어서 기업의 자원의 제약과 같은 현실적인 면이 고려되었다고 할 수 없기 때문에 목표계획법의 적용이 검토된다. 또한 다기준 의사결정의 상황에서 목표기준의 모호함이 기존의 목표계획법에서는 반영되지 않기 때문에 이러한 점을 고려하여 퍼지집합을 적용한 모형을 구축하고자 하였다.

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PSO 최적화 기법을 이용한 Ethylene Oxide Plant 배치에 관한 연구 (The Research of Optimal Plant Layout Optimization based on Particle Swarm Optimization for Ethylene Oxide Plant)

  • 박평재;이창준
    • 한국안전학회지
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    • 제30권3호
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    • pp.32-37
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    • 2015
  • In the fields of plant layout optimization, the main goal is to minimize the construction cost including pipelines as satisfying all constraints such as safety and operating issues. However, what is the lacking of considerations in previous researches is to consider proper safety and maintenance spaces for a complex plant. Based on the mathematical programming, MILP(Mixed Integer Linear Programming) problems including various constraints can be formulated to find the optimal solution which is to achieve the best economic benefits. The objective function of this problem is the sum of piping cost, pumping cost and area cost. In general, many conventional optimization solvers are used to find a MILP problem. However, it is really hard to solve this problem due to complex inequality and equality constraints, since it is impossible to use the derivatives of objective functions and constraints. To resolve this problem, the PSO (Particle Swarm Optimization), which is one of the representative sampling approaches and does not need to use derivatives of equations, is employed to find the optimal solution considering various complex constraints in this study. The EO (Ethylene Oxide) plant is tested to verify the efficacy of the proposed method.

선박용 체크밸브의 최적설계에 관한 연구 (A Study on the Optimization Design of Check Valve for Marine Use)

  • 이춘태
    • 동력기계공학회지
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    • 제21권6호
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    • pp.56-61
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    • 2017
  • The check valves are mechanical valves that permit fluids to flow in only one direction, preventing flow from reversing. It is classified as one way directional valves. There are various types of check valves that used in a marine application. A lift type check valve uses the disc to open and close the passage of fluid. The disc lift up from seat as pressure below the disc increases, while drop in pressure on the inlet side or a build up of pressure on the outlet side causes the valve to close. An important concept in check valves is the cracking pressure which is the minimum upstream pressure at which the valve will operate. On the other hand, optimization is a process of finding the best set of parameters to reach a goal while not violating certain constraints. The AMESim software provides NLPQL(Nonlinear Programming by Quadratic Lagrangian) and genetic algorithm(GA) for optimization. NLPQL is the implementation of a SQP(sequential quadratic programming) algorithm. SQP is a standard method, based on the use of a gradient of objective functions and constraints to solve a non-linear optimization problem. A characteristic of the NLPQL is that it stops as soon as it finds a local minimum. Thus, the simulation results may be highly dependent on the starting point which user give to the algorithm. In this paper, we carried out optimization design of the check valve with NLPQL algorithm.

로봇팔의 장애물 중에서의 시간 최소화 궤도 계획 (Minimum-Time Trajectory Planning for a Robot Manipulator amid Obstacles)

  • 박종근
    • 한국정밀공학회지
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    • 제15권1호
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    • pp.78-86
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    • 1998
  • This paper presents a numerical method of the minimum-time trajectory planning for a robot manipulator amid obstacles. Each joint displacement is represented by the linear combination of the finite-term quintic B-splines which are the known functions of the path parameter. The time is represented by the linear function of the same path parameter. Since the geometric path is not fixed and the time is linear to the path parameter, the coefficients of the splines and the time-scale factor span a finite-dimensional vector space, a point in which uniquely represents the manipulator motion. The displacement, the velocity and the acceleration conditions at the starting and the goal positions are transformed into the linear equality constraints on the coefficients of the splines, which reduce the dimension of the vector space. The optimization is performed in the reduced vector space using nonlinear programming. The total moving time is the main performance index which should be minimized. The constraints on the actuator forces and that of the obstacle-avoidance, together with sufficiently large weighting coefficients, are included in the augmented performance index. In the numerical implementation, the minimum-time motion is obtained for a planar 3-1ink manipulator amid several rectangular obstacles without simplifying any dynamic or geometric models.

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