• Management Science and Financial Engineering
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• v.20 no.1
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• pp.17-19
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• 2014

In this paper, we consider an interesting variant of the inverse minimum traveling salesman problem. Given an instance (G, w) of the minimum traveling salesman problem defined on a metric space, we fix a specified Hamiltonian cycle $HC_0$. The task is then to adjust the edge cost vector w to w' so that the new cost vector w' satisfies the triangle inequality condition and $HC_0$ can be returned by the minimum spanning tree algorithm in the TSP-instance defined with w'. The objective is to minimize the total deviation between the original and the new cost vectors with respect to the $L_1$-norm. We call this problem the inverse metric traveling salesman problem against the minimum spanning tree algorithm and show that it is closely related to the inverse metric spanning tree problem.

• Journal of the military operations research society of Korea
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• v.35 no.3
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• pp.47-59
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• 2009

A hybrid Genetic Algorithm is applied to military resource allocation problem. Since military uses many resources in order to maximize its ability, optimization technique has been widely used for analysing resource allocation problem. However, most of the military resource allocation problems are too complicate to solve through the traditional operations research solution tools. Recent innovation in computer technology from the academy makes it possible to apply heuristic approach such as Genetic Algorithm(GA), Simulated Annealing(SA) and Tabu Search(TS) to combinatorial problems which were not addressed by previous operations research tools. In this study, a hybrid Genetic Algorithm which reinforces GA by applying local search algorithm is introduced in order to address military optimization problem. The computational result of hybrid Genetic Algorithm on Missile Interceptor Allocation problem demonstrates its efficiency by comparing its result with that of a simple Genetic Algorithm.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1-8
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• 2006

This paper considers an interesting transportation problem where trailers and tractors are involved in moving material. We identified a class of combinatorial optimization problems for minimizing the number of tractors and trailers required to accommodate the transportation needs. Then, we show that the fundamental problem is NP-hard and analyze its properties to develop efficient heuristic to handle the problem effectively.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.519-524
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• 1996

This paper describes the application of Genetic Algorithms(GAs) to nonlinear constrained mixed optimization problems. Genetic Algorithms are combinatorial in nature, and therefore are computationally suitable for treating discrete and integer design variables. But, several problems that conventional GAs are ill defined are applicaiton of penalty function that can be adapted to transform a constrained optimization problem into an unconstrained optimization problem into an unconstrained one and premature convergence of solution. Thus, we developed an modified GAs to solve this problems, and two examples are given to demonstrate the effectiveness of the methodology developed in this paper.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.623-625
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• 1998

This paper focuses on the fast convergence in nonlinear parameter optimization which is necessary for the fitting of nonlinear models to data. The simulated annealing(SA) and genetic algorithm(GA), which are widely used for combinatorial optimization problems, are stochastic strategy for search of the ground state and a powerful tool for optimization. However, their main disadvantage is the long convergence time by unnecessary extra works. It is also recognised that gradient-based nonlinear programing techniques would typically fail to find global minimum. Therefore, this paper develops a modified SA which is the SDS(Stochastic deterministic stochastic) algorithm can minimize cost function of optimal problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.71-78
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• 2013

An efficient vehicle routing heuristic for different vehicle moving times for forward and backward between two points is studied in this research. Symmetric distance or moving times are assumed to move back and forth between two points in general, but it is not true in reality. Also, various moving speeds along time zones are considered such as the moving time differences between rush hours or not busy daytimes. To solve this type of extremely complicated combinatorial optimization problems, delivery zones are specified and delivery orders are determined for promising results on the first stage. Then delivery orders in each zone are determined to be connected with other zones for a tentative complete delivery route. Improvement steps are followed to get an effective delivery route for unsymmetric-time-varing vehicle moving speed problems. Performance evaluations are done to show the effectiveness of the suggested heuristic using computer programs specially designed and developed using C++.

• Smart Structures and Systems
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• v.31 no.3
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• pp.247-257
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• 2023

In structural health monitoring of large-scale structures, optimal sensor placement plays an important role because of the high cost of sensors and their supporting instruments, as well as the burden of data transmission and storage. In this study, a vibration sensor placement algorithm based on deep reinforcement learning (DRL) is proposed, which can effectively solve non-convex, high-dimensional, and discrete combinatorial sensor placement optimization problems. An objective function is constructed to estimate the quality of a specific vibration sensor placement scheme according to the modal assurance criterion (MAC). Using this objective function, a DRL-based algorithm is presented to determine the optimal vibration sensor placement scheme. Subsequently, we transform the sensor optimal placement process into a Markov decision process and employ a DRL-based optimization algorithm to maximize the objective function for optimal sensor placement. To illustrate the applicability of the proposed method, two examples are presented: a 10-story braced frame and a sea-crossing bridge model. A comparison study is also performed with a genetic algorithm and particle swarm algorithm. The proposed DRL-based algorithm can effectively solve the discrete combinatorial optimization problem for vibration sensor placements and can produce superior performance compared with the other two existing methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.1-6
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• 2020

Fast service access involves keeping track of the location of mobile users, while they are moving around the mobile network for a satisfactory level of QoS (Quality of Service) in a cost-effective manner. The location databases are used to keep track of Mobile Terminals (MT) so that incoming calls can be directed to requested mobile terminals at all times. MT reporting cell system used in location management is to designate each cell in the network as a reporting cell or a non-reporting cell. Determination of an optimal number of reporting cells (or reporting cell configuration) for a given network is reporting cell planning (RCP) problem. This is a difficult combinatorial optimization problem which has an exponential complexity. We can see that a cell in a network is either a reporting cell or a non-reporting cell. Hence, for a given network with N cells, the number of possible solutions is 2^N. We propose a biogeography based optimization (BBO) for design of mobile station location management system in wireless communication network. The number and locations of reporting cells should be determined to balance the registration for location update and paging operations for search the mobile stations to minimize the cost of system. Experimental results show that our proposed BBO is a fairly effective and competitive approach with respect to solution quality for optimally designing location management system because BBO is suitable for combinatorial optimization and multi-functional problems.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.5
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• pp.427-436
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• 2010

This paper deals with a task assignment problem of multiple UAVs performing multiple tasks on multiple targets. The task assignment problem of multiple UAVs is a kind of combinatorial optimization problems such as traveling salesman problem or vehicle routing problem, and it has NP-hard computational complexity. Therefore, computation time increases as the size of considered problem increases. To solve the problem efficiently, approximation methods or heuristic methods are widely used. In this study, the problem is formulated as a mixed integer linear program, and is solved by a mixed integer linear programming and a genetic algorithm, respectively. Numerical simulations for the environment of the multiple targets, multiple tasks, and obstacles were performed to analyze the optimality and efficiency of each method.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.471-477
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• 2005

This paper proposes an efficient constraint-based optimization model for the design of 3G mobile networks, such as universal mobile telecommunications system (UMTS). The model concerns about finding a set of sites for locating radio network controllers (RNCs) from a set of pre-defined candidate sites, and at the same time optimally assigning node Bs to the selected RNCs. All these choices must satisfy a set of constraints and optimize an objective function. This problem is NP-hard and consequently cannot be practically solved by exact methods for real size networks. Thus, this paper proposes a hybrid search strategy for tackling this complex and combinatorial optimization problem. The proposed hybrid search strategy is composed of three phases: A constraint satisfaction method with an embedded problem-specific goal which guides the search for a good initial solution, an optimization phase using local search algorithms, such as tabu algorithm, and a post­optimization phase to improve solutions from the second phase by using a constraint optimization procedure. Computational results show that the proposed search strategy and the model are highly efficient. Optimal solutions are always obtained for small or medium sized problems. For large sized problems, the final results are on average within $5.77\%$ to $7.48\%$ of the lower bounds.