• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.69.2-69
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• 2002

Process measurements contain random and gross errors and the size estimation of gross errors is required for production accounting. Mixed integer programming technique had been applied to identify and estimate the gross errors simultaneously. However, the compensate model based on mixed integer programming used all measured variables or spanning tree as gross error candidates. This makes gross error estimation problem combinatorial or computationally expensive. Mixed integer programming with test statistics is proposed for computationally inexpensive gross error identification /estimation. The gross error candidates are identified by measurement test and the set of gross error candidates are...

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.1-8
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• 2002

A Branch-and-Cut algorithm is a branch-and-bound algorithm in which rutting planes are generated throughout the branch-and-bound tree. It is now one of the most widespread and successful methods for solving mixed integer programming problems. In this paper we presents efficient implementation techniques of branch-and-cut algorithm for miked integer programming problems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.102-106
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• 2004

The purpose of this paper is to develope a large-scale mixed integer program MIPBB. In this paper, the various issues such as branching strategies, searching and bounding strategies, storing basis information, handling numerical instability, that are important for developing a large-scale mixed integer programming program, are considered. And the experimental results of MIPBB are presented and compared to those of GLPK.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.1
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• pp.13-27
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• 2014

In this paper, we deal with a Steiner Ring Star (SRS) problem arising from the design of survivable telecommunication networks. We develop two mixed integer programming formulations for the SRS problem by implementing Miller-Tucker-Zemlin (MTZ) and Sarin-Sherali-Bhootra (SSB) subtour elimination constraints, and then apply the reformulation-linearization technique (RLT) to enhance the lower bound obtained by the LP relaxation. By exploiting the ring-star structure of underlying network, we devise some valid inequalities that tighten the LP relaxation. Computational results demonstrate the effectiveness of the proposed solution procedure.

• Journal of Korean Society for Quality Management
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• v.38 no.3
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• pp.333-339
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• 2010

It is difficult to determine an optimal inventory level of aircraft engine and modules to achieve the target operational availability since F100-PW-200 & 229 engines of the F-16 & KF-16 aircraft are consisted of 5 modules with different failure rates and costs. This study presents a decision model, combining an integer programming problem and a regression metamodel. Data for the metamodel was attained from results of a simulation model, that represents operational and repair process of F-16 and KF-16. The objective function of an integer programming problem is maximizing the operational availability, representing pessimistic circumstances. Finally, an integer programming problem with a metamodel can make an optimal decision of the inventory level.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.27-35
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• 2009

Integer programming is a very effective technique for searching optimal solution of combinatorial optimization problems. However, its applicability is limited to linear models. In this paper, I propose an effective method for solving a nonlinear optimization problem by integrating the powerful search performance of integer programming and the flexibility of neighborhood search algorithms. In the first phase, integer programming is executed with subproblem which can be represented as a linear form from the given problem. In the second phase, a neighborhood search algorithm is executed with the whole problem by taking the result of the first phase as the initial solution. Through the experimental results using a nonlinear maximal covering problem, I confirmed that such a simple integration method can produce far better solutions than a neighborhood search algorithm alone. It is estimated that the success is primarily due to the powerful performance of integer programming.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.5
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• pp.1069-1084
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• 2011

This paper considers an optimal base station clustering problem for designing a mobile (wireless) communication network. For a given network with a set of nodes (base stations), the problem is to optimally partition the set of nodes into subsets (each called a cluster) such that the associated inter-cluster traffic is minimized under certain topological constraints and cluster capacity constraints. In the problem analysis, the problem is formulated as an integer programming problem. The integer programming problem is then transformed into a binary integer programming problem, for which the associated linear programming relaxation is solved in a column generation approach assisted by a branch-and-bound procedure. For the column generation, both a heuristic algorithm and a valid inequality approach are exploited. Various numerical examples are solved to evaluate the effectiveness of the LP (Linear Programming) based branch-and-bound algorithm.

• Journal of applied mathematics & informatics
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• v.19 no.1_2
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• pp.371-384
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• 2005

Usual symmetric duality results are proved for Wolfe and Mond-Weir type nondifferentiable nonlinear symmetric dual programs under F-convexity F-concavity and F-pseudoconvexity F-pseudoconcavity assumptions. These duality results are then used to formulate Wolfe and Mond-Weir type nondifferentiable minimax mixed integer dual programs and symmetric duality theorems are established. Moreover, nondifferentiable fractional symmetric dual programs are studied by using the above programs.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.366-368
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• 2011

In this paper, we introduce a new kind of expressions for privacy protection techniques in database, such as K-anonymity L-diversity and t-closeness. With such kind of expressions, we provide a new way to solve the privacy protection problems, such as Linear programming, Non-linear programming, Integer programming and so on. Also most of the heuristic techniques are also efficient to be adopted under the expressions given.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.47-55
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• 2010

Linear constraint satisfaction optimization problem is a kind of combinatorial optimization problem involving linearly expressed objective function and complex constraints. Integer programming is known as a very effective technique for such problem but require very much time and memory until finding a suboptimal solution. In this paper, we propose a method to improve the search performance by integrating local search and integer programming. Basically, simple hill-climbing search, which is the simplest form of local search, is used to solve the given problem and integer programming is applied to generate a neighbor solution. In addition, constraint programming is used to generate an initial solution. Through the experimental results using N-Queens maximization problems, we confirmed that the proposed method can produce far better solutions than any other search methods.