• Journal of the Korean Operations Research and Management Science Society
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• v.11 no.2
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• pp.79-87
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• 1986

This paper illustrates a new method to solve the fuzzy goal programming (FGP) problem. It is proved that the FGP proposed by Narasimhan can be solved on the basis of linear programming(LP) model. Narasimhan formulated the FGP problem as a set of $S^{K}$LP problems, each containing 3K constraints, where K is the number of fuzzy goals/constraints. Whereas Hanna formulated the FGP problem as a single LP problem with only 2K constraints and 2K + 1 additional variables. This paper presents that the FGP problem can be transformed with easy into a single LP model with 2K constraints and only one additional variables. And we propose extended FGP :(1) FGP with weights associated with individual goals, (2) FGP with preemptive prioities. The extended FGP has a framework that is identical to that of conventional goal programming (GP), such that the extended FGP can be applied with fuzzy concept to the all areas where GP can be applied.d.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.09a
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• pp.497-504
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• 2003

An optimal design method for hybrid structural control system of building structures subject to earthquake excitation is presented in this paper. Designing a hybrid structural control system nay be defined as a process that optimizes the capacities and configuration of passive and active control systems as well as structural members. The optimal design proceeds by formulating the optimization problem via a multi-stage goal programming technique and, then, by finding reasonable solution to the optimization problem by means of a goal-updating genetic algorithm. The process of the integrated optimization design is illustrated by a numerical simulation of a nine-story building structure subject to earthquake excitation. The effectiveness of the proposed method is demonstrated by comparing the optimally designed results with those of a hybrid structural control system where structural members, passive and active control systems are uniformly distributed.

• Journal of the Korean Operations Research and Management Science Society
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• v.11 no.1
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• pp.36-43
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• 1986

In this study, the fuzzy programming is extended to handle various types of membership functions by transformation of the complicated fuzzy programming problems into the equivalent crisp linear programming problems with single objective. It is well-known that the fuzzy programming problem with linear membership functions (i.e., ramp type) can be easily transformed into a linear programming problem by introducing one dummy variable to minimize the worst unwanted deviation. However, until recently not many researches have been done to handle various general types of complicated linear membership functions which might be more realistic than ramp-or triangular-type functions. In order to handle these complicated membership functions, the goal dividing concept, which is based on the fuzzy set operation (i. e., intersection and union operations), has been prepared. The linear model obtained using the goal dividing concept is more efficient and single than the previous models [4, 8]. In addition, this result can be easily applied to any nonlinear membership functions by piecewise approximation since the membership function is continuous and monotone increasing or decreasing.

• Journal of the Earthquake Engineering Society of Korea
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• v.7 no.5
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• pp.93-102
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• 2003

An optimal design method for hybrid structural control system of building structures subject to earthquake excitation is presented in this paper. Designing a hybrid structural control system may be defined as a process that optimizes the capacities and configuration of passive and active control systems as well as structural members. The optimal design proceeds by formulating the optimization problem via a multi-stage goal programming technique and, then, by finding reasonable solution to the optimization problem by means of a goal-updating genetic algorithm. In the multi-stage goal programming, design targets(or goals) are at first selected too correspond too several stages and the objective function is th n defined as the sum of the normalized distances between these design goals and each of the physical values, that is, the inter-story drifts and the capacities of the control system. Finally, the goal-updating genetic algorithm searches for optimal solutions satisfying each stage of design goals and, if a solution exists, the levels of design goals are consecutively updated to approach the global optimal solution closest too the higher level of desired goals. The process of the integrated optimization design is illustrated by a numerical simulation of a nine-story building structure subject to earthquake excitation. The effectiveness of the proposed method is demonstrated by comparing the optimally designed results with those of a hybrid structural control system where structural members, passive and active control systems are uniformly distributed.

• Journal of Forest and Environmental Science
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• v.28 no.1
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• pp.30-36
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• 2012

The total yield of Pinus koraiensis stands was reviewed along forest function by using goal programming, which is one of the operations research techniques. The 4 kinds of management goals are set to identify timber production in the Research Forest of Kangwon National University. As a result, scenario 1 was estimated the best timber production over 2,073 ha area and also 588 ha in the third quarter was showed the most timber harvest. The rate of timber harvest was separated by 10 to 50 percent in non-timber forest function in the scenario 1 and that model was applied to the Research Forest of Kangwon National University. The structure of the area and volume is showed to be balanced quarterly when rate of timber harvest at 10 to 20 percent.

• Korean Management Science Review
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• v.14 no.1
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• pp.1-10
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• 1997

We consider a problem of estimating the economics of scale for the natural gas industries. The goal programming/constrained regression is employed for estimating the economics of scale for the natural gas industry, and the problem is formulated as a linear programing problem. Also the translog cost function is used to represent the cost structure for the natural gas industry. The Korean Gas Corporation was selected as a case study, and we demonstrate that the suggested goal programming/constrained regression approach is appropriate for estimating the economies of scale for the Korean nature gas industry.

• Journal of the Korean Operations Research and Management Science Society
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• v.11 no.1
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• pp.51-58
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• 1986

The propose of this study is to presnet a generalized linear/goal programming software, which has been developed to run on mickrocomputers with at least 512 K bytes of memory. The main characteristics of our algorithm for solving LP/GP problems are outlined as follows ; First, it uses the revised simplex algorithm, which is the most efficient computational procedure for computers. Second, it employs the sparse matrix technique to overcome the limited memory of microcomputers. Last, it uses the modified product form of invers (MPFI) to reduce round-off errors. The test runs with our code written in FORTRAN show that it can be used as an effective tool for solving linear/goal programming problems of considerable size.

• Journal of applied mathematics & informatics
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• v.13 no.1_2
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• pp.323-334
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• 2003

This paper presents a goal-programming (GP) model for management decision-making for sugarcane fertilizer mix problems. Sensitivity analysis on the priority structure of the goals has been performed to obtain all possible solutions. The study uses Euclidean distance function to measure distances of all possible solutions from the ideal solution. The optimum solution is determined from the minimum distance between the ideal solution and other possible solutions of the problem. The optimum solution corresponds to the appropriate priority structure of the problem in the decision-making context. furthermore, the results obtained from sensitivity analysis on the cost of combination of fertilizers confirm the priority structure.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.1
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• pp.113-122
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• 1994

This study proposes a new procedure to find an initial solution to reduce the number of iterations of goal programming. The process of computing an initial solution is divided into two steps in this study. Decision variables which satisfy feasibility using Gaussian eliminations construct an initial solution reducing the iterations in the first step. It uses LHS as a tool that decision variables construct an initial solution. The initial solution which is constructed by the first step computes the updated coefficient of the objective function in the second step. If the solution does not satisfy the optimality, the optimal solution using the Modified Simplex Method is sought. The developed method doesn't reduce the overall computing time of goal programming problems, because time is more required for the process of constructing an initial solution. But The result of this study shows that the proposed procedure can reduce the large number of iterations in the first step effectively.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.2
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• pp.63-77
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• 2015

The territorial air that our sovereignty is being applied to is divided into several zones and areas. In order to use and manage them effectively, these zones and areas have laws, regulations and rules. The number of airspaces (MOA : Military Operation Area) and Ranges that are used in the military are limited and many airbases are being used for training. Currently the central department manages some, and the rest are independent airbases. Therefore, efficient allocation is not performed and the result occurs where airspaces and ranges are allocated unnecessarily. This is increasing the workload of staff leading to unnecessary re-work. To slove problem, this study proposed models for allocating airspaces and ranges using goal programming with multi-objective functions of minimizing the deviations of the target values.