• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.65-68
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• 2000

The problem of selecting a portfolio is to find Un investment plan that achieves a desired return while minimizing the risk involved. One stream of algorithms are based upon mixed integer linear programming models and guarantee an integer optimal solution. But these algorithms require too much time to apply to real problems. Another stream of algorithms are fur a near optimal solution and are fast enough. But, these also have a weakness in that the solution generated can't be guaranteed to be integer values. Since it is not a trivial job to tansform the scullion into integer valued one simutaneously maintaining the quality of the solution, they are not easy to apply to real world portfolio selection. To tackle the problem more efficiently, we propose an algorithm which generates a very good integer solution in reasonable amount of time. The algorithm is tested using Korean stock market data to verify its accuracy and efficiency.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2187-2195
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• 2017

In order to solve the AC optimal power flow (OPF) problem considering the generators' on/off status, it is necessary to model the problem as mixed-integer nonlinear programming (MINLP). Because the computation time to find the optimal solution to the mixed-integer AC OPF problem increases significantly as the system becomes larger, most of the existing solutions simplify the problem either by deciding the on/off status of generators using a separate unit commitment algorithm or by ignoring the minimum output of the generators. Even though this kind of simplification may make the overall computation time tractable, the results can be significantly erroneous. This paper proposes a novel algorithm for the mixed-integer AC OPF problem, which can provide a near-optimal solution quickly and efficiently. The proposed method is based on a combination of the outer approximation method and the relaxed AC OPF theory. The method is applied to a real-scale power system that has 457 generators and 2132 buses, and the result is compared to the branch-and-bound (B&B) method and the genetic algorithm. The results of the proposed method are almost identical to those of the compared methods, but computation time is significantly shorter.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.1
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• pp.58-65
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• 2002

When we estimate the optimal solution satisfy the objective function and subjective equation in the integer programming, The optimal solution of the objective function Z is decided by the positive integer at extreme point or revised extreme point in the convex set. The convex set is made up the linear subjective equation. The purpose of the paper is thus to establish a stepwise optimization in the integer programming model by estimating the variation △C/sub j/ of the constant term C/sub j/ in the linear objective function, after an application of the modified Branch & Bound method.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.81-84
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• 2003

The ability to determine the optimal frequencies and offsets for independent and unrestricted ordering cycles for multiple items can be very valuable for managing storage capacity constrained facilities in a supply chain. The complexity of this problem has resulted in researchers focusing on more tractable surrogate problems that are special cases of the base problem. Murthy et al. (European Journal of Operation Research 2003) developed insights leading to solution of the original problem and present a heuristic for offsetting independent and unrestricted ordering cycles for items to minimize their joint storage requirements. However, their study cannot find optimal solution due to the Greedy Heuristic solution procedure. In this paper, we present a complete procedure to find the optimal solution for the model with a integer programming optimization approach and genetic algorithm. Numerical examples are included to compare each model with that of Murthy et at. Research of this type may prove useful in solving the more general problem of selecting order policies to minimize combined holding, ordering, and storage costs.

• Management & Information Systems Review
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• v.29 no.4
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• pp.109-121
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• 2010

In this study, we propose a solution procedure to solve travelling salesman problem(TSP) with special cost function, route dependent travelling salesman problem(RDTSP). First, we develop an integer programming model to describe the problem. In the model, a variable means a possible route. And, the number of variables in this model are extremely large. So, we develop a LP relaxation problem of the IP model and solve the relaxation problem by a column generation technique. The relaxation problem does not guarantee the optimal solution. If we get an integer solution in the ralaxation problem, then the solution is an optimal one. But, if not, we cannot get an optimal solution. So, we approach a branch and price technique. The overall solution procedure can be applied a printed circuit board(PCB) assembly process.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.306-310
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• 2004

In this paper, we consider the Ring Loading Problem with integer demand splitting (RLP). The problem is given with a ring network, in which a required traffic requirement between each selected node pair must be routed on it. Each traffic requirement can be routed in both directions on the ring network while splitting each traffic requirement in two directions only by integer is allowed. The problem is to find an optimal routing of each traffic requirement which minimizes the capacity requirement. Here, the capacity requirement is defined as the maximum of traffic loads imposed on each link on the network. We formulate the problem as an integer program. By characterizing every extreme point solution to the LP relaxation of the formulation, we show that the optimal objective value of the LP relaxation is equal to p or p+0.5, where p is a nonnegative integer. We also show that the difference between the optimal objective value of RLP and that of the LP relaxation is at most 1. Therefore, we can verify that the optimal objective value of RLP is p+1 if that of the LP relaxation is p+0.5. On the other hand, we present a strengthened LP with size polynomially bounded by the input size, which provides enough information to determine if the optimal objective value of RLP is p or p+1.

• Journal of Korean Society of Transportation
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• v.14 no.4
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• pp.7-29
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• 1996

The optimal solution obtained by a linear programming model is to maximize the ramp inflow rate. It is argued in this paper that the maximization of inflow rate is different from the maximization of outflow rate under congested conditions. Therefore, this paper proposes a systematic searching procedure from a linear programing formulation to a integer programming : first obtain the optimal solution by a linear programming and then adding weight to linear programming then. solve the optimal solution again by integer programming i.e. The proposed method is an interactive approach. Measure of effectiveness by simulation models regards the real time data(O/D, queue, delay, etc), can be utilized in the proposed interactive process.

• Journal of the military operations research society of Korea
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• v.23 no.1
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• pp.14-24
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• 1997

This study is to develop an optimal surveillance units assignment model in order to obtain the maximized surveillance efficiency with the limited surveillance units. There are many mathematical models which deal with problems to assign weapons such as aircrafts, missiles and guns to targets. These models minimize the lost required to attack, the threat forecast from the enemy, or both of them. However, a problem of the efficient assignment of surveillance units is not studied yet, nevertbless it is important in the battlefield surveillance system. This paper is concerned with the development of the optimal surveillance units assignment model using integer programming. An optimal integer solution of the model can be obtained by using linear programming and branch and bound method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.131-138
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• 1995

A new technique using integer programming based on fuzzy multi-criteria function is proposed for generator maintenance scheduling. Minimization maintenance delay cost and maximization reserve power are considered for fuzzy multi-criteria function. To obtain an optimal solution for generator maintenance scheduling under fuzzy environment, fuzzy multi-criteria integer programming is used. In the maintenance scheduling, a characteristic feature of the presented approach is that the crisp constraints with uncertainty can be taken into account by using fuzzy set theory and so more flexible solution can be obtained. The effectiveness of the proposed approach is demonstrated by the simulation results.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.613-616
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• 2000

This paper considers the problem of subway crew scheduling. Crew scheduling is concerned with finding a minimum number of assignments of crews to a given timetable satisfying various restrictions. Traditionally, crew scheduling problem has been formulated as a set covering or set partitioning problem possessing exponentially many variables, but even the LP relaxation of the problem is hard to solve due to the exponential number of variables. In this paper, we propose two basic techniques that solve the problem in a reasonable time, though the optimality of the solution is not guaranteed. To reduce the number of variables, we adopt column-generation technique. We could develop an algorithm that solves column-generation problem in polynomial time. In addition, the integrality of the solution is accomplished by variable-fixing technique. Computational results show column-generation makes the problem of treatable size, and variable fixing enables us to solve LP relaxation in shorter time without a considerable increase in the optimal value. Finally, we were able to obtain an integer optimal solution of a real instance within a reasonable time.