• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.641-646
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• 2001

Eigenstructure assignment is a typical method with the capability of consideration of the time-domain specifications in designing a linear control system. In this paper, we propose a new method for eigenstructure to achieve desired eigenvectors more precisely than with the conventional method. In the proposed method, the conventional eigenstructure assignment problem is interpreted as a constrained optimization one, and it converted into an unconstrained optimization problem to deal with the problem easily. Numerical examples are presented to illustrate the proposed flexible eigenstructure assignment method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.3
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• pp.339-347
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• 1993

The channel assignment problem in a mobile communication system is a NP-complete combinatorial optimization problem, in which the calculation time increases exponentially as the range of the problem is extended. This paper adapts a conventional Hopfield neural network model to the channel assignment problem to relieve the calculation time by means of the parallelism supplied from the neural network. In the simulation study, we checked the feasability of such a parallel method for the fixed channel assignment with uniform, and nouniform channel requirements, and for the dynamic channel assignment with considering continously varying channel requirements.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.159-164
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• 2022

The linear assignment problem (LAP) and linear bottleneck assignment problem (LBAP) has been unknown the algorithm to solve the optimal solution within polynomial-time. These problems are classified by NP-hard. Therefore, we can be apply metaheuristic methods or linear programming (LP) software package or Hungarian algorithm (HA) with O(m⁴) computational complexity. This paper suggests polynomial time algorithm with O(mn)=O(m²),m=n time complexity to LAP and LBAP. The select-delete method is simply applied to LAP, and the delete-select method is used to LBAP. For the experimental data without the unique algorithm can be apply to whole data, the proposed algorithm can be obtain the optimal solutions for whole data.

• Journal of Korean Institute of Industrial Engineers
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• v.5 no.1
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• pp.45-51
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• 1979

This paper presents a heuristic solution procedure for the quadratic assignment problem, ranking procedure, which can handle the case where not all of the facilities have the same size of area. The ranking procedure is found more efficient than two other existing heuristic procedures from results of computational experience. Based on the out-patients flow pattern and the procedure developed, an improved layout of the three existing general hospitals is developed with the objective of minimizing the total distance travelled by patients in the central and the out-patient clinic.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1030-1033
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• 2006

In this paper, the assignment problem is considered. We propose an approach based on the solution of a sequence of shortest path sub-problem. We extend the cost reduction method, which is used for finding initial assignment, to solve these sub-problems. The use of the extended reduction method makes it possible to devise an efficient multi-augmenting algorithm.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.939-947
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• 2008

The paper proposes a new formulation of the output feedback pole assignment problem. In this formulation, a unified approach is presented for solving the pole assignment problem with various additional objectives. These objectives include optimizing a variety of performance indices, and imposing constraints on the output feedback matrix structure, e.g. decentralized structure. Conditions for the existence of the output feedback are discussed. However, the thrust of the paper is on the development of a convergent pole assignment algorithm. It is shown that when exact pole assignment is not possible, the method can be used to place the poles close to the desired locations. Examples are provided to illustrate the method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.201-207
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• 2018

There is no known polynomial time algorithm for random-type quadratic assignment problem(RQAP) that is a NP-complete problem. Therefore the heuristic or meta-heuristic approach are solve the approximated solution for the RQAP within polynomial time. This paper suggests polynomial time algorithm for random type quadratic assignment problem (QAP) with time complexity of $O(n^2)$. The proposed algorithm applies one-to-one matching strategy between ascending order of sum of distance for each location and descending order of sum of quantity for each facility. Then, swap the facilities for reflect the correlation of distances of locations and quantities of facilities. For the experimental data, this algorithm, in spite of $O(n^2)$ polynomial time algorithm, can be improve the solution than genetic algorithm a kind of metaheuristic method.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.37-45
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• 2022

In this paper, a multi agent-multi task assignment problem, which is a representative problem of combinatorial optimization, is presented. The objective of the problem is to determine the coordinated agent-task assignment that maximizes the sum of the achievement rates of each task. The achievement rate is represented as a concave down increasing function according to the number of agents assigned to the task. The problem is expressed as an NP-hard problem with a non-linear objective function. In this paper, to solve the assignment problem, we propose a hybrid cross-entropy algorithm as an effective and efficient solution methodology. In fact, the general cross-entropy algorithm might have drawbacks (e.g., slow update of parameters and premature convergence) according to problem situations. Compared to the general cross-entropy algorithm, the proposed method is designed to be less likely to have the two drawbacks. We show that the performances of the proposed methods are better than those of the general cross-entropy algorithm through numerical experiments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.163-171
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• 2013

This paper proposes an algorithm that seeks the optimal solution for an assignment problem through a simplified process. Generally it is Hungarian algorithm that is prevalently used to solve a given assignment problem. The proposed algorithm reduces 4 steps Hungarian algorithm into 2 steps. Firstly, the algorithm selects the minimum cost from a matrix and deletes the rest of the rows and columns. Secondly, it improves on the solution through reassignment process. For 27 balanced assignment problems and 7 unbalanced problems, the proposed algorithm has successfully yielded the optimal solution, which Genetic algorithm has failed. This algorithm is thus found to be an appropriate replacement of Hungarian algorithm.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.253-259
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• 2013

Investment scenarios in the transportation network design problem usually contain installation or expansion of multi-mode transportation links. When one applies the mode choice analysis and traffic assignment sequentially for each investment scenario, it is possible that the travel impedance used in the mode choice analysis is different from the user equilibrium cost of the traffic assignment step. Therefore, to estimate the travel impedance and mode choice accurately, one needs to develop a combined model for the mode choice and traffic assignment. In this paper, we derive the inverse demand and the excess demand functions for the multi-mode multinomial logit mode choice function and develop a combined model for the multi-mode variable demand traffic assignment problem. Using data from the regional O/D and network data provided by the KTDB, we compared the performance of the partial linearization algorithm with the Frank-Wolfe algorithm applied to the excess demand model and with the sequential heuristic procedures.