• The Journal of Information Systems
• /
• v.5
• /
• pp.21-41
• /
• 1996

Linear programming has become an important tool in decision-making of modern business management. This remarkable growth can be traced to the pioneering efforts of many individuals and research organizations. The popular using of personal computers make it very easy to process those complicated linear programming models. Furthermore advanced linear programming software packages assist us to solve L.P. models without any difficult process. Even though the advanced L.P. professional packages, the needs of more detailed deterministic elements for business decisions have forced us to apply dynamic approaches for more resonable solutions. For the purpose of these problems applying to the "Mathematica" packages which is composed of mathematic tools, the simplex processes show us the flexible and dynamic decision elements included to any other professional linear programming tools. Especially we need proper dynamic variables to analyze the shadow prices step by step. And applying SAS(Statistical Analysis System) packages to the L.P. problems, it is also one of the best way to get good solution. On the way trying to the other L.P. packages which are prepared for Spreadsheets i.e., MS-Excel, Lotus-123, Quatro etc. can be applied to linear programming models. But they are not so much useful for the problems. Calculating simplex tableau is an important method to interpret L.P. format for the optimal solution. In this paper we find out that the more detailed and efficient techniques to interpret useful software of mathematica and SAS for business decision making of linear programming. So it needs to apply more dynamic technique of using of Mathematica and SAS multiple software to get more efficient deterministic factors for the sophiscated L.P. solutions.

• Management Science and Financial Engineering
• /
• v.14 no.2
• /
• pp.25-44
• /
• 2008

This paper concentrates on reduction of a Quadratic Fractional Integer Programming Problem (QFIP) to a 0-1 Mixed Linear Programming Problem (0-1 MLP). The solution technique is based on converting the integer variables to binary variables and then the resulting Quadratic Fractional 0-1 Programming Problem is linearized to a 0-1 Mixed Linear Programming problem. It is illustrated with the help of a numerical example and is solved using the LINDO software.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.3 no.1
• /
• pp.75-79
• /
• 1978

The use of three mathematical programming techniques (quadratic programming, integer quadratic programming and linear programming) is discussed to solve some problems in linear regression analysis. When the criterion is the minimization of the sum of squared deviations and the parameters are linearly constrained, the problem may be formulated as quadratic programming problem. For the selection of variables to find "best" regression equation in statistics, the technique of integer quadratic programming is proposed and found to be a very useful tool. When the criterion of fitting a linear regression is the minimization of the sum of absolute deviations from the regression function, the problem may be reduced to a linear programming problem and can be solved reasonably well.ably well.

• 전기의세계
• /
• v.22 no.5
• /
• pp.12-18
• /
• 1973

This paper deals with Linear Programming by Signal Flow Graph technique which is different from that of Mason and Coates. The objective function is regarded as variable, and slack variable node, artificial variable node and objective function variable (constant) node are newly defined, which shows the process for optimization of solution very intuitively. Also methods for solving L.P. and examples with subject to Ax.leq.b, Ax=b and Ax.geq.b are presented.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2008.10a
• /
• pp.65-75
• /
• 2008

Over the last three decades, there are many researches focusing on the practice and theory of RM in airlines. Most of them have dealt with a seat assignment problem for maximizing the total revenue. In this study, we focus on a seat assignment problem in airlines. The seat assignment problem can be modeled as a stochastic programming model which is difficulty to solve optimally. However, with some assumptions on the demand distribution functions and a linear approximation technique, we can transform the complex stochastic programming model to a Linear Programming model. Some computational experiments are performed to evaluate out model with randomly generated data. They show that our model has a good performance comparing to existing models, and can be considered as a basis for further studies on improving existing seat assignment models.

• Korean Management Science Review
• /
• v.26 no.3
• /
• pp.117-131
• /
• 2009

Over the last three decades, there are many researches focusing on the practice and theory of RM in airlines. Most of them have dealt with a seat assignment problem for maximizing the total revenue. In this study, we focus on a seat assignment problem in airlines. The seat assignment problem can be modeled as a stochastic programming model which is difficulty to solve optimally. However, with some assumptions on the demand distribution functions and a linear approximation technique, we can transform the complex stochastic programming model to a Linear Programming model. Some computational experiments are performed to evaluate out model with randomly generated data. They show that our model has a good performance comparing to existing models, and can be considered as a basis for further studies on improving existing seat assignment models.

• Proceedings of the KIEE Conference
• /
• 2006.07a
• /
• pp.262-263
• /
• 2006

This paper presents a scheme to solve the congestion problem with phase-shifting transformer(PST) and power generation using linear programming method. A good design of PST and power generation control can improve total transfer capability(TTC) in interconnected systems. This paper deals with an application of optimization technique for TTC calculation. linear programming method is used to maximize power flow of tie line subject to security constraints such as voltage magnitude and real power flow. The proposed method is applied to 10 machines 39 buses model systems to show its effectiveness.

• 제어로봇시스템학회:학술대회논문집
• /
• 1986.10a
• /
• pp.137-141
• /
• 1986

This paper presents optimal recursive digital filter design to meet simultaneous specifications of magnitude and linear phase characteristics. As is well known, the overshoot in the vicinity of discontinuity is hight. The technique using linear programming (the dual programming) is choosing more specification points in the vicinity of band limit frequency. The resulting filter can shown improved response and numerical accuracy with reduced nonuniform specification points in frequency domain.

• Proceedings of the KIEE Conference
• /
• 2004.05a
• /
• pp.98-101
• /
• 2004

Linear Programming(LP) is the term used for defining a wide range of optimization problems in which the objective function to be minimized or maximized is linear in the unknown variables and the constraints are a combination of linear equalities and inequalities. LP problems occur in many real-life economic situations where profits are to be maximized or costs minimized with constraint limits on resources. While the simplex method introduced in a later reference can be used for hand solution of LP problems, computer use becomes necessary even for a small number of variables. Problems involving diet decisions, transportation, production and manufacturing, product mix, engineering limit analysis in design, airline scheduling, and so on are solved using computers. This technique is called Sequential Linear Programming (SLP). This paper describes LP's problems and solves a LP's problems using the neural networks.

• Journal of the Korean Society for Railway
• /
• v.8 no.5
• /
• pp.391-397
• /
• 2005

In this paper we present a model for the maintenance routing construction and the trainset allocation (maintenance routing problem). The model solves the maintenance routing problem using column generation algorithm which was used to combine constraint programming and linear programming. Ilog-cplex was used to solve the linear programming model and Ilog-solver was used to solve the constraint programming model. The computational experience is also provided.