• Korean Management Science Review
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• v.25 no.1
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• pp.43-53
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• 2008

This paper considers a profit-based unit commitment problem with fuel consumption constraint and maintenance cost, which is one of the key decision problems in electricity industry. The nature of non-linearity inherent in the constraints and objective functions makes the problem intractable which have led many researches to focus on Lagrangian based heuristics. To solve the problem more effectively, we propose mixed integer programming based solution algorithm linearizing the complex non-linear constraints and objectives functions. The computational experiments using the real-world operation data taken from a domestic electricity power generator show that the proposed algorithm solves the given problem effectively.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.220-223
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• 2003

This paper presents VSCOPF(Votage Stability Constrained Optimal Power Flow) algorithm based on SLP(Successive Linear Programming) to interpret the large scale system. Voltage stability index used to this paper is L index to be presented by function form. The objective function consists of load shedding cost minimization. Voltage stability indicator constraint was incorporated in traditional OPF formulation. as well as the objective function and constraints are linearlized and the optimal problem is performed by SLP(Successive Linear Programming). In this paper, the effect of voltage stability limit constraint is showed in the optimal load curtailment problems. As a result, an optimal solution is calculated to minimize load shedding cost guaranteeing voltage security level. Numerical examples using IEEE 39-bus system is also presented to illustrate the capabilities of the proposed formulation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.3
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• pp.17-23
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• 2008

In this research article, scheduling a casting sequence in a job-shop type foundry involving a variety of casts made of an identical alloy but with different shapes and II weights, has been investigated. The objective is to produce the assigned mixed orders satisfying due dates and obtaining the highest ingot efficiency simultaneously. Implementing simple integer programming instead of complicated genetic algorithms accompanying rigorous calculations proves that it can provide a feasible solution with a high accuracy for a complex, multi-variable and multi-constraint optimization problem. Enhancing the ingot efficiency under the constraint of discrete ingot sizes is accomplished by using a simple and intelligible algorithm in a standard integer programming. Employing this simple methodology, a job-shop type foundry is able to maximize the furnace utilization and minimize ingot waste.

• ETRI Journal
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• v.41 no.5
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• pp.619-625
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• 2019

In this paper, we propose an array pattern synthesis scheme using semidefinite programming (SDP) under array excitation power constraints. When an array pattern synthesis problem is formulated as an SDP problem, it is known that an additional rank-one constraint is generated inevitably and relaxed via semidefinite relaxation. If the solution to the relaxed SDP problem is not of rank one, then conventional SDP-based array pattern synthesis approaches fail to obtain optimal solutions because the additional rank-one constraint is not handled appropriately. To overcome this drawback, we adopted a bisection technique combined with a penalty function method. Numerical applications are presented to demonstrate the validity of the proposed scheme.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.165-187
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• 2004

Constraint Programming in AS and Optimization in OR started and have grown in different backgrounds to solve common decision-making problems in real world. This paper tries to integrate results from those different fields by suggesting a hybrid solver as an integration framework. Starting with an integrating modeling language, a way to implement a hybrid solver will be discussed using Warren's abstract machine and an finite domain constraint programming solver structures. This paper will also propose some issues rising when implementing the hybrid solver and provide their solutions.

• Bulletin of the Korean Mathematical Society
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• v.23 no.2
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• pp.141-148
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• 1986

In [1], M. Guignard considered a constraint set in a Banach space, which is similar to that in [2] and gave a first order necessary optimality condition which generalized the Kuhn-Tucker conditions [3]. Sufficiency is proved for objective functions which is either pseudoconcave [5] or quasi-concave [6] where the constraint sets are taken pseudoconvex. In this note, we consider a psedoconvex programming problem in a Hilbert space. Constraint set in a Hillbert space being pseudoconvex and the objective function is restrained by an operator equation. Then we use the methods similar to that in [1] and [6] to obtain a necessary and sufficient optimality condition.

• IE interfaces
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• v.16 no.4
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• pp.485-495
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• 2003

This paper reports on the application of the integration of mathematical programming model and database in a decision support system (DSS) for the planning of the multi-reservoir operating system. The DSS is based on a multi-objective, mixed-integer goal programming (MIGP) model, which can generate efficient solutions via the weighted-sums method (WSM). The major concern of this study is seamless, efficient integration between the mathematical model and the database, because there are significant differences in structure and content between the data for a mathematical model and the data for a conventional database application. In order to load the external optimization results on the database, we developed a systematic way of naming variable/constraint so that a rapid identification of variables/constraints is possible. An efficient database structure for planning of the multi-reservoir operating system is presented by taking advantage of the naming convention of the variable/constraint.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1395-1407
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• 2011

When a Sequential Quadratic Programming (SQP) method is used to solve the nonlinear programming problems, one of the main difficulties is that the Quadratic Programming (QP) subproblem may be incompatible. In this paper, an SQP algorithm is given by modifying the traditional QP subproblem and applying a class of $l_{\infty}$ penalty function whose penalty parameters can be adjusted automatically. The new QP subproblem is compatible. Under the extended Mangasarian-Fromovitz constraint qualification condition and the boundedness of the iterates, the algorithm is showed to be globally convergent to a KKT point of the non-linear programming problem.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1157-1165
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• 2011

The Karush-Kuhn-Tucker (KKT) necessary optimality conditions for nonlinear differentiable programming problems are also sufficient under suitable convexity assumptions. The KKT conditions in multiobjective programming problems with interval-valued objective and constraint functions are derived in this paper. The main contribution of this paper is to obtain the Pareto optimal solutions by resorting to the sufficient optimality condition.

• Journal of the military operations research society of Korea
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• v.25 no.1
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• pp.133-140
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• 1999

Preprocessing for the -01 integer programming can reduce the size of problem instance as well as tighten its linear programming relaxations. In this research, the preprocessing techniques are classified into two categories. First, for the reduction of problem size, there are variable fixing and constraint elimination techniques. Second, for the reduction of feasible region, there are coefficient reduction and Euchidean reduction techniques. These methods are implemented and the effects are shown by experimental results.