• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.254-257
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• 2002

In previous study about combinatorial optimization problem solver by using neural network, since Hopfield method, to converge into the optimum solution sooner and certainer is regarded as important. Namely, only static states are considered as the information. However, from a biological point of view, the dynamical system has lately attracted attention. Then we propose the "dynamical" combinatorial optimization problem solver using hysteresis neural network. In this article, the proposal system is evaluated by the N-Queen problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.201-208
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• 2010

This research is about developing an efficient solution procedure for the vehicle routing problem under varying vehicle moving speeds for hour-based time interval. Different moving speeds for every hour is too difficult condition to solve for this type of combinatorial optimization problem. A methodology to divide the 12 hour based time interval offered by government into 5 different time intervals and then divide delivery area into 12 small divisions first and then re-organizing them into 5 groups. Then vehicle moving speeds are no longer varying in each of the 5 divisions. Therefore, a typical TSP solution procedure may be applied to find the shortest path for all 5 divisions and then connect the local shortest paths to form a delivery path for whole area. Developed solution procedures are explained in detail with 60 points example.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.189-197
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• 1999

Assembly line balancing problem is known as one of difficult combinatorial optimization problems. This problem has been solved with linear programming, dynamic programming approaches. but unfortunately these approaches do not lead to efficient algorithms. Recently, genetic algorithm has been recognized as an efficient procedure for solving hard combinatorial optimization problems, but has a defect that requires long-run time and computational complexties to find the solution. For this reason, we adapt a new method called the Critical Node Method that is intuitive, easy to understand, simple for implementation. Fuzzy set theory is frequently used to represent uncertainty of information. In this paper, to treat the data of real world problems we use a fuzzy number to represent the duration and Critical Node Method based heuristic procedure is developed for solving fuzzy assembly line balancing problem.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.961-979
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• 2018

For many years, matching in a bipartite graph has been widely used in various assignment problems, such as stable marriage problem (SMP). As an application of bipartite matching, the problem of stable marriage is defined over equally sized sets of men and women to identify a stable matching in which each person is assigned a partner of opposite gender according to their preferences. The classical SMP proposed by Gale and Shapley uses preference lists for each individual (men and women) which are infeasible in real world applications for a large populace of men and women such as matrimonial websites. In this paper, we have proposed an enhancement to the SMP by computing a weighted score for the users registered at matrimonial websites. The proposed enhancement has been formulated into profit maximization of matrimonial websites in terms of their ability to provide a suitable match for the users. The proposed formulation to maximize the profits of matrimonial websites leads to a combinatorial optimization problem. We have proposed greedy and genetic algorithm based approaches to solve the proposed optimization problem. We have shown that the proposed genetic algorithm based approaches outperform the existing Gale-Shapley algorithm on the dataset crawled from matrimonial websites.

• Korean Management Science Review
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• v.15 no.2
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• pp.153-159
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• 1998

Clustering or classification is a very fundamental task that may occur almost everywhere for the purpose of grouping. Optimal clustering is an example of very complicated combinatorial optimization problem and it is hard to develop a generally applicable optimal algorithm. In this paper we propose a general-purpose algorithm for the optimal clustering based on SA(simulated annealing). Among various iterative global optimization techniques imitating natural phenomena that have been proposed and utilized successfully for various combinatorial optimization problem, simulated annealing has its superiority because of its convergence property and simplicity. We first present a version of accelerated simulated annealing(ASA) and then we apply ASA to develop an efficient clustering algorithm. Application examples are also given.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.892-894
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• 2005

Graph partitioning provides an important tool for data clustering, but is an NP-hard combinatorial optimization problem. Spectral clustering where the clustering is performed by the eigen-decomposition of an affinity matrix [1,2]. This is a popular way of solving the graph partitioning problem. On the other hand, semidefinite relaxation, is an alternative way of relaxing combinatorial optimization. issuing to a convex optimization[4]. In this paper we present a semidefinite programming (SDP) approach to graph equi-partitioning for clustering and then we use eigen-decomposition to obtain an optimal partition set. Therefore, the method is referred to as semidefinite spectral clustering (SSC). Numerical experiments with several artificial and real data sets, demonstrate the useful behavior of our SSC. compared to existing spectral clustering methods.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.308-311
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• 2008

The ant colony optimization (ACO) is a metaheuristic inspired by the behavior of real ants. Recently, ACO has been widely used to solve the difficult combinatorial optimization problems. In this paper, we propose an ACO algorithm to solve the two disjoint paths problem with dual link cost structure (TDPDCP). We propose a dual pheromone structure and a procedure for solution construction which is appropriate for the TDPDCP. Computational comparisons with the state-of-the-arts algorithms are also provided.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1992.04b
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• pp.73-73
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• 1992

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.27-34
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• 2023

The optimal assignment problem between agents and tasks is known as one of the representative problems of combinatorial optimization and an NP-hard problem. This paper covers multi agent-multi task assignment problems with uncertain completion probability. The completion probabilities are generally uncertain due to endogenous (agent or task) or exogenous factors in the system. Assignment decisions without considering uncertainty can be ineffective in a real situation that has volatility. To consider uncertain completion probability mathematically, a mathematical formulation with stochastic programming is illustrated. We also present an algorithm by using the sample average approximation method to solve the problem efficiently. The algorithm can obtain an assignment decision and the upper and lower bounds of the assignment problem. Through numerical experiments, we present the optimality gap and the variance of the gap to confirm the performances of the results. This shows the excellence and robustness of the assignment decisions obtained by the algorithm in the problem with uncertainty.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.27-35
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• 2021

Local search has been used to solve various combinatorial optimization problems. One of the most important factors in local search is the method of generating a neighbor solution. In this paper, we propose neighbor generation strategies of local search for permutation-based combinatorial optimization, and compare the performance of each strategies targeting the traveling salesman problem. In this paper, we propose a total of 10 neighbor generation strategies. Basically, we propose 4 new strategies such as Rotation in addition to the 4 strategies such as Swap which have been widely used in the past. In addition, there are Combined1 and Combined2, which are made by combining basic neighbor generation strategies. The experiment was performed by applying the basic local search, but changing only the neighbor generation strategy. As a result of the experiment, it was confirmed that the performance difference is large according to the neighbor generation strategy, and also confirmed that the performance of Combined2 is the best. In addition, it was confirmed that Combined2 shows better performance than the existing local search methods.