• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.25
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• pp.41-51
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• 1992

Most of the planing models for automated manufacturing systems are based on the assumption that for each part there is only one process method available Really. for a part to be manufactured in an automated manufacturing system, a number of different process methods can be generated, each of which may require specific types of tools and auxiliary devices such as fixtures, grippers and feeders. In this paper, An optimal algorithm for the selection of a set of process methods with the minimum corresponding manufacturing cost and minimal number of tools and auxiliary devices Is proposed. The proposed optimal algorithm is based on branch and bound method which is one of the optimal solution methods.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.781-783
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• 1997

Genetic Algorithm is a kind of an evolution programming based on natural evolution principle. It applied to probabilistic searching, machine learning and optimization, and many good results were reported. Generator maintenance scheduling is an optimization Problem with constraints. This paper applied a genetic algorithm to generator maintenance scheduling problem and tested on sample systems. The results are compared with heuristic method and branch-and-bound method.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.221-227
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• 2019

It has long been known that weapon target assignment (WTA) problem is NP-hard. Nonetheless, an exact solution can be found using Brute-Force or branch-and bound method which utilize approximation. Many heuristic algorithms, genetic algorithm particle swarm optimization, etc., have been proposed which provide near-optimal solutions in polynomial time. This paper suggests polynomial time algorithm that can be obtain the optimal solution of WTA problem for the number of total weapons k, the number of weapon types m, and the number of targets n. This algorithm performs k times for O(mn) so the algorithm complexity is O(kmn). The proposed algorithm can be minimize the number of trials than brute-force method and can be obtain the optimal solution.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.2
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• pp.43-49
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• 1992

Edge coloring problem is to find a minimum cardinality coloring of the edges of a graph so that any pair of edges incident to a common node do not have the same colors. Edge coloring problem is NP-hard, hence it is unlikely that there exists a polynomial time algorithm. We formulate the problem as a covering of the edges by matchings and find valid inequalities for the convex hull of feasible solutions. We show that adding the valid inequalities to the linear programming relaxation is enough to determine the minimum coloring number(chromatic index). We also propose a method to use the valid inequalities as cutting planes and do the branch and bound search implicitly. An example is given to show how the method works.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.109-117
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• 2014

This paper revises the Quine-McCluskey Algorithm to circuit minimization problems. Quine-McCluskey method repeatedly finds the prime implicant and employs additional procedures such as trial-and-error, branch-and-bound, and Petrick's method as a means of circuit minimization. The proposed algorithm, on the contrary, produces an implicant chart beforehand to simplify the search for the prime implicant. In addition, it determines a set cover to streamline the search for $1^{st}$ and $2^{nd}$ essential prime implicants. When applied to 3-variable and 4-variable experimental data, the proposed algorithm has indeed proved to obtain the optimal solutions much more simply and accurately than the Quine-McCluskey method.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.506-516
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• 2005

This paper deals with an investment scheduling problem of maximizing net present value of dividend with reinvestment allowed, where each investment has certain capital requirement and generates deterministic profit. Such deterministic profit is calculated at completion of each investment and then allocated into two parts, including dividend and reinvestment, at each predetermined reinvestment time point. The objective is to make optimal scheduling of investments over a fixed planning horizon which maximizes total sum of the net present values of dividends subject to investment precedence relations and capital limit but with reinvestment allowed. In the analysis, the scheduling problem is transformed to a kind of parallel machine scheduling problem and formulated as an integer programming which is proven to be NP-complete. Thereupon, a depth-first branch-and-bound algorithm is derived. To test the effectiveness and efficiency of the derived algorithm, computational experiments are performed with some numerical instances. The experimental results show that the algorithm solves the problem relatively faster than the commercial software package (CPLEX 8.1), and optimally solves the instances with up to 30 investments within a reasonable time limit.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.601-614
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• 2006

In this paper, a study on task assignment strategies for a complex real-time network system is presented. Firstly, two task assignment strategies are proposed to improve previous strategies. The proposed strategies assign tasks with meeting end-to-end real-time constraints, and also with optimizing system utilization through period modulation of the tasks. Consequently, the strategies aim at the optimizationto optimize of system performance with while still meeting real-time constraints. The proposed task assignment strategies are devised using the genetic algorithmswith heuristic real-time constraints in the generation of new populations. The strategies are differentiated by the optimization method of the two objectives-meeting end-to-end real-time constraints and optimizing system utilization: the first one has sequential genetic algorithm routines for the objectives, and the second one has one multiple objective genetic algorithm routine to find a Pareto solution. Secondly, the performances of the proposed strategies and a well-known existing task assignment strategy using the BnB(Branch and Bound) optimization are compared with one other through some simulation tests. Through the comparison of the simulation results, the most adequate task assignment strategies are proposed for some as system requirements-: the optimization of system utilization, the maximization of running tasktasks, and the minimization of the number of network node nodesnumber for a network system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.179-184
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• 2002

We consider the heterogeneous fleet vehicle routing problem (HVRP), a variant of the classical vehicle routing problem (VRP). The HVRP differs from the classical VRP in that it deals with a heterogeneous fleet of vehicles having various capacities, fixed costs, and variables costs. Therefore the HVRP is to find the fleet composition and a set of routes with minimum total cost. We give an integer programming formulation of the problem and propose an algorithm to solve it. Although the formulation has exponentially many variables, we can efficiently solve the linear programming relaxation of it by using the column generation technique. To generate profitable columns we solve a shortest path problem with capacity constraints using dynamic programming. After solving the linear programming relaxation, we apply a branch-and-bound procedure. We test the proposed algorithm on a set of benchmark instances. Test results show that the algorithm gives best-known solutions to almost all instances.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.3
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• pp.155-166
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• 2010

I review research on self-sensing and structural optimizations of laminated carbon/epoxy composites in Japan. Self-sensing is one of the multiple functions of composites; i.e., carbon fiber is used as a sensor as well as reinforcement. I present a controversial issue in self-sensing and detail research results. Structural optimization of laminated CFRP composites is indispensable in reducing the weights of modern aerospace structural components. I present a modified efficient global search method using the multi-objective genetic algorithm and fractal branch and bound method. My group has focused its research on these subjects and our research results are presented here.