• Industrial Engineering and Management Systems
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• v.15 no.1
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• pp.32-40
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• 2016

This paper considers a special case of a three machine flow shop scheduling problem in which operation processing time of each job is ordered such that machine 1 has the longest processing time, whereas machine 3, the shortest processing time. The objective of the problem is the minimization of the total completion time. Although the problem is simple, its complexity is yet to be established to our best knowledge. This paper first introduces the problem and presents some optimal properties of the problem. Then, it establishes several special cases in which a polynomial-time optimal solution procedure can be found. In addition, the paper proves that the recognition version of the problem is at least binary NP-complete. The complexity of the problem has been open despite its simple structure and this paper finally establishes its complexity. Finally, a simple and intuitive heuristic is developed and the tight worst case bound on relative error of 6/5 is established.

• Computers and Concrete
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• v.24 no.5
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• pp.469-488
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• 2019

In this research study, the artificial neural networks approach is used to estimate the ultimate shear capacity of reinforced concrete beams with transverse reinforcement. More specifically, surrogate approaches, such as artificial neural network models, have been examined for predicting the shear capacity of concrete beams, based on experimental test results available in the pertinent literature. The comparison of the predicted values with the corresponding experimental ones, as well as with available formulas from previous research studies or code provisions highlight the ability of artificial neural networks to evaluate the shear capacity of reinforced concrete beams in a trustworthy and effective manner. Furthermore, for the first time, the (quantitative) values of weights for the proposed neural network model, are provided, so that the proposed model can be readily implemented in a spreadsheet and accessible to everyone interested in the procedure of simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9A
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• pp.788-794
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• 2005

In this paper, we focus on the potential of dual polarized antennas in mobile system. thus, this paper designs exact dual polarized channel with Spatial Channel Model (SCM) and investigates the performance for certain environment. Using proposed the channel model; we know estimates of the channel capacity as a function of cross polarization discrimination (XPD) and spatial fading correlation. It is important that the MIMO channel matrix consists of Kronecker product dividable spatial and polarized channel. Through the channel characteristics, we propose an algorithm for the adaptation of transmit antenna configuration to time varying propagation environments. The optimal active transmit antenna subset is determined with equal power allocated to the active transmit antennas, assuming no feedback information on types of the selected antennas. We first consider a heuristic decision strategy in which the optimal active transmit antenna subset and its system capacity are determined such that the transmission data rate is maximized among all possible types. This paper then proposes singular values decision procedure consisting of Kronecker product with spatial and polarize channel. This method of singular value decision, which the first channel environments is determined using singular values of spatial channel part which is made of environment parameters and distance between antennas. level of correlation. Then we will select antenna which have various polarization type. After spatial channel structure is decided, we contact polarization types which have considerable cases It is note that the proposed algorithms and analysis of dual polarized channel using SCM (Spatial Channel Model) optimize channel capacity and reduce the number of transmit antenna selection compare to heuristic method which has considerable 100 cases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.560-569
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• 2005

This paper proposes an algorithm for transmit antenna selection in a multi-input multi-output(MIMO) spatial multiplexing system with per antenna rate control(PARC) and an ordered successive interference cancellation (OSIC) receiver. The active antenna subset is determined at the receiver and conveyed to the transmitter using feedback information on transmission rate per antenna. We propose a serial decision procedure consisting of a successive process that tests whether antenna selection gain exists when the antenna with the lowest pre-processing signal to interference and noise ratio(SINR) is discarded at each stage. Furthermore, we show that 'reverse detection ordering', whereby the signal with the lowest SINR is decoded at each stage of successive decoding, widens the disparities among fractions of the whole capacity allocated to each individual antenna and thus maximizes a gain of antenna selection. Numerical results show that the proposed reverse detection ordering based serial antenna selection approaches the closed-loop MIMO capacity and that it induces a negligible capacity loss compared with the heuristic selection strategy even with considerably reduced complexity.

• Steel and Composite Structures
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• v.25 no.6
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• pp.663-670
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• 2017

The problem of layup optimization of the composite laminates involves a very complex multidimensional solution space which is usually non-exhaustively explored using different heuristic computational methods such as genetic algorithms (GA). To ensure the convergence to the global optimum of the applied heuristic during the optimization process it is necessary to evaluate a lot of layup configurations. As a consequence the analysis of an individual layup configuration should be fast enough to maintain the convergence time range to an acceptable level. On the other hand the mechanical behavior analysis of composite laminates for any geometry and boundary condition is very convoluted and is performed by computational expensive numerical tools such as finite element analysis (FEA). In this respect some studies propose very fast FEA models used in layup optimization. However, the lower bound of the execution time of FEA models is determined by the global linear system solving which in some complex applications can be unacceptable. Moreover, in some situation it may be highly preferred to decrease the optimization time with the cost of a small reduction in the analysis accuracy. In this paper we explore some machine learning techniques in order to estimate the failure of a layup configuration. The estimated response can be qualitative (the configuration fails or not) or quantitative (the value of the failure factor). The procedure consists of generating a population of random observations (configurations) spread across solution space and evaluating using a FEA model. The machine learning method is then trained using this population and the trained model is then used to estimate failure in the optimization process. The results obtained are very promising as illustrated with an example where the misclassification rate of the qualitative response is smaller than 2%.

• Progress in Medical Physics
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• v.15 no.2
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• pp.84-93
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• 2004

Stereotactic radiosurgery (SRS) is a technique that delivers a high dose to a target legion and a low dose to a critical organ through only one or a few irradiations. For this purpose, many mathematical methods for optimization have been proposed. There are some limitations to using these methods: the long calculation time and difficulty in finding a unique solution due to different tumor shapes. In this study, many clinical target shapes were examined to find a typical pattern of tumor shapes from which some possible ideal geometrical shapes, such as spheres, cylinders, cones or a combination, are assumed to approximate real tumor shapes. Using the arrangement of multiple isocenters, optimum variables, such as isocenter positions or collimator size, were determined. A database was formed from these results. The optimization procedure consisted of the following steps: Any shape of tumor was first assumed to an ideal model through a geometry comparison algorithm, then optimum variables for ideal geometry chosen from the predetermined database, followed by a final adjustment of the optimum parameters using the real tumor shape. Although the result of applying the database to other patients was not superior to the result of optimization in each case, it can be acceptable as a plan starling point.

• Journal of the Korean Geographical Society
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• v.33 no.1
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• pp.57-74
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• 1998

The essay examines the development and characteristics of regionalism as new social-political conflicts in Western Europe since 1970's and explores theoretical approaches of regionalism in terms of modemization theory of social sciences. There are various types of regionalistic movements: separatism, regional equity development, federalism, autonomy, nationalism, and so on. These different orientations have already shown serious problems, theoretical and conceptional, conceming the analysis of regionalism. But in conceptualizing this phenomenon, five competitive theses could be distinguished: persistence thesis, differentiation thesis, political procedure thesis, convergence thesis, and counter-differentiation thesis. Although the heuristic theses are still elaborated on the base of empirical studies and actual data, they indicate that each thesis ontologises the region as one of the key concepts explaining regionalism very differently.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.12
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• pp.856-864
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• 2019

The weapon-target assignment (WTA) problem is a matter of effectively allocating weapons to a number of threats. The WTA in a rapidly changing dynamic environment of engagement must take into account both of properties of the threat and the weapon and the effect of the previous decision. We propose a method of applying the Greedy Randomized Adaptive Search Procedure (GRASP) algorithm, a kind of meta-heuristic method, to derive optimal solution for a dynamic WTA problem. Firstly, we define a dynamic WTA problem and formulate a mathematical model for applying the algorithm. For the purpose of the assignment strategy, the objective function is defined and time-varying constraints are considered. The dynamic WTA problem is then solved by applying the GRASP algorithm. The optimal solution characteristics of the formalized dynamic WTA problem are analyzed through the simulation, and the algorithm performance is verified via the Monte-Carlo simulation.

• Journal of Information Technology Applications and Management
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• v.18 no.4
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• pp.81-93
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• 2011

By using GOSST theory, this paper models SSCFLP taking FLP, capacity of the facilities, single source capacitated limitation level and service enhancement issues into consideration. GOSST theory is strongly suggested as the solution procedure for these issues. We have used clustering of Center of Gravity method using the case study of the company S and then, took a heuristic GOSST measure in the alternative selection process. As a result, the research finds an alternative solution that both meets the satisfactory level of service and achieves consistent distribution capacity. When using this modeling, especially, to select the location of the logistics distribution center, the efficiency of current facilities is maximized while offering the minimum geometric distance for the alternative. Also, we can expect that the illustrated model and alternative solution can be applied to architecture of distribution system, to selection of telecommunication system locations for wireless network and to relocation of related facilities due to their sensitivities to location and weight.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.253-260
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• 1999

The recycling cell formation problem means that disposal products me classified into recycling part families using group technology in their end of life phase. Disposal products have the uncertainties of product status by usage influences during product use phase and recycling cells are formed design, process and usage attributes. In order to treat the uncertainties, fuzzy set theory and fuzzy logic-based neural network model are applied to recycling cell formation problem far disposal products. In this paper, a heuristic approach fuzzy ART neural network is suggested. The modified fuzzy ART neural network is shown that it has a great efficiency and give an extension for systematically generating alternative solutions in the recycling cell formation problem. We present the results of this approach applied to disposal refrigerators and the comparison of performances between other algorithms. This paper introduced a procedure which integrates economic and environmental factors into the disassembly of disposal products for recycling in recycling cells. A qualitative method of disassembly analysis is developed and its ai is to improve the efficiency of the disassembly and to generated an optimal disassembly which maximize profits and minimize environmental impact. Three criteria established to reduce the search space and facilitate recycling opportunities.