• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.3
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• pp.23-38
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• 2015

In this paper, we evaluate the strength of three families of cutting planes for a lot-sizing problem. Lot-sizing problem is very basic MIP model for production planning and many strong valid inequalities have been developed for a variety of relaxations in the literature. To use three families of cutting planes in Branch-and-Cut framework, we develop separation algorithms for each cut and implement them in CPLEX. Then, we perform computational study to compare the effectiveness of three cuts for randomly generated instances of the lot-sizing problem.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.697-708
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• 1995

For many practical optimization problems where the system components are stochastic, the objective functions can not be represented analytically. Furthermore, many of these problems are characterized by the presence of multiple and conflicting objectives. In this research, we introduce a new algorithm through an interactive cutting plane method for solving this multi-criteria simulation optimization problem. Then a turning process is evaluated through the proposed algorithm.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.3
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• pp.227-236
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• 1996

In this paper an algorithm based on cutting plane approach is developed which constructs all the efficient p-tuples of multiobjective integer linear fractional programming problem. The integer solution is constrained to satisfy and h out of n additional constraint sets. A numerical illustration in support of the proposed algorithm is developed.

• Journal of KIISE:Information Networking
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• v.35 no.6
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• pp.497-509
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• 2008

Various algorithms and architectures for efficient packet classification have been widely studied. Packet classification algorithms based on a decision tree structure such as HiCuts and HyperCuts are known to be the best by exploiting the geometrical representation of rules in a classifier. However, the algorithms are not practical since they involve complicated heuristics in selecting a dimension of cuts and determining the number of cuts at each node of the decision tree. Moreover, the cutting is not efficient enough since the cutting is based on regular interval which is not related to the actual range that each rule covers. In this paper, we proposed a new efficient packet classification algorithm using a range cutting. The proposed algorithm primarily finds out the ranges that each rule covers in 2-dimensional prefix plane and performs cutting according to the ranges. Hence, the proposed algorithm constructs a very efficient decision tree. The cutting applied to each node of the decision tree is optimal and deterministic not involving the complicated heuristics. Simulation results for rule sets generated using class-bench databases show that the proposed algorithm has better performance in average search speed and consumes up to 3-300 times less memory space compared with previous cutting algorithms.

• Industrial Engineering and Management Systems
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• v.10 no.3
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• pp.221-231
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• 2011

One of the critical issues in wireless sensor network is the design of a proper routing protocol. One possible approach is utilizing a virtual infrastructure, which is a subset of sensors to connect all the sensors in the network. Among the many virtual infrastructures, the connected dominating set is widely used. Since a small connected dominating set can help to decrease the protocol overhead and energy consumption, it is preferable to find a small sized connected dominating set. Although many algorithms have been suggested to construct a minimum connected dominating set, there have been few exact approaches. In this paper, we suggest an improved optimal algorithm for the minimum connected dominating set problem, and extensive computational results showed that our algorithm outperformed the previous exact algorithms. Also, we suggest a new heuristic algorithm to find the connected dominating set and computational results show that our algorithm is capable of finding good quality solutions quite fast.

• Journal of Navigation and Port Research
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• v.30 no.7
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• pp.561-566
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• 2006

In this paper, we propose a new ship scheduling set packing model considering limited risk or variance. The set packing model is used in many applications, such as vehicle routing, crew scheduling, ship scheduling, cutting stock and so on. As long as the ship scheduling is concerned, there exits many unknown external factors such as machine breakdown, climate change and transportation cost fluctuation. However, existing ship scheduling models have not considered those factors apparently. We use a quadratic set packing model to limit the variance of expected cost of ship scheduling problems under stochastic spot rates. Set problems are NP-complete, and additional quadratic constraint makes the problems much harder. We implement Kelley's cutting plane method to replace the hard quadratic constraint by many linear constrains and use branch-and-bound algorithm to get the optimal integral solution. Some meaningful computational results and comments are provided.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.3
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• pp.11-22
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• 1997

The aim of this paper is to develop the B & B type algorithms for globally minimizing concave function under 0-1 knapsack constraint. The linear convex envelope underestimating the concave object function is introduced for the bounding operations which locate the vertices of the solution set. And the simplex containing the solution set is sequentially partitioned into the subsimplices over which the convex envelopes are calculated in the candidate problems. The adoption of cutting plane method enhances the efficiency of the algorithm. These mean valid inequalities with respect to the integer solution which eliminate the nonintegral points before the bounding operation. The implementations are effectively concretized in connection with the branching stategys.

• Proceedings of the IEEK Conference
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• summer
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• pp.400-404
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• 2004

There are many methods to find solutions for Integer Programming problems (IPs) such as the Branch-Bound philosophy or the Cutting Plane algorithm. However, most of them have a problem that is the explosion of sets in the computing process. In addition, GA is known as a heuristic search algorithm for solutions of optimization problems. It is started from a random initial guess solution and attempting to find one that is the best under some criteria and conditions. The paper will study an artificial intelligent method to solve IPs by using Genetic Algorithms (GAs). The original solution of this was presented in the papers of Fabricio Olivetti de Francaand and Kimmo Nieminen [2003]. However, both have several limitations which causes could be operations in GAs. The paper proposes a method to upgrade these operations and computational results are also shown to support these upgrades.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.5 s.305
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• pp.51-58
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• 2005

This paper proposes an efficient algorithm for extracting a tumor region from an breast ultrasound volume image by using rate-distortion (R-D) based seeded region growing. In the proposed algorithm the rate and the distortion represent the roughness of the contour and the dissimilarity of pixels in a region, respectively. Staring from an initial seed region set in each cutting plane of a volume, a pair of the seed region and one of adjacent regions whose R-D cost is minimal is searched and then they are merged into a new updated seed region. This procedure is recursively performed until the averaged R-D cost values per the number of contour pixels in the seed region becomes maxim. As a result, the final seed region has good pixel homogeneity and a much smooth contour. Finally, the tumor volume is extracted using the contours of the final seed regions in all the cutting planes. Experimental results show that the averaged error rate of the proposed method is shown to be below 4%.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1992.04b
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• pp.117-126
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• 1992

This study proposes a new interactive multicriteria method for determining the best levels of the decision variables needed to optimize a stochastic computer simulation with multiple response variables. The method, called the Pairwise Comparison Stochastic Cutting Plane (PCSCP) method, combines good features from interactive multiple objective mathematical programming methods and response surface methodology. The major characteristics of the PCSCP algorithm are: (1) it interacts progressively with the decision maker (DM) to obtain his preferences, (2) it uses good experimental design to adequately explore the decision space while reducing the burden on the DM, and (3) it uses the preference information provided by the DM and the sampling error in the responses to reduce the decision space. This paper presents the basic concepts of the PCSCP method along with its performance for solving randomly selected test problems.