• Proceedings of the Korea Society for Industrial Systems Conference
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• 1999.05a
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• pp.145-153
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• 1999

We consider a Direct Input Output Manufacturing System(DIOMS) which has a munber of machine centers placed along a built-in Automated Storage/Retrieval System(AS/RS). The Storage/Retrieval (S/R) machine handles parts placed on pallets for the machine centers located at either one or both sides of the As/Rs. This report studies the operational aspect of DIOMS and determines the optimal operating policy by combining computer simulation and genetic algorithm. The operational problem includes: input sequencing control, dispatching rule of the S/R machine, machine center-based part type selection rule, and storage assignment policy. For each operating policy, several different policies are considered based on the known research results. In this report, using the computer simulation and genetic algorithm we suggest a method which gives the optimal configuration of operating policies within reasonable computation time.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.80-89
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• 2014

A stochastic optimal power flow (S-OPF) model considering uncertainties of load and wind power is developed based on chance constrained programming (CCP). The difficulties in solving the model are the nonlinearity and probabilistic constraints. In this paper, a limit relaxation approach and an iterative learning control (ILC) method are implemented to solve the S-OPF model indirectly. The limit relaxation approach narrows the solution space by introducing regulatory factors, according to the relationship between the constraint equations and the optimization variables. The regulatory factors are designed by ILC method to ensure the optimality of final solution under a predefined confidence level. The optimization algorithm for S-OPF is completed based on the combination of limit relaxation and ILC and tested on the IEEE 14-bus system.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.5 s.98
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• pp.29-39
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• 1999

Many industrial products can be assembled in various sequences of assembly operations. To save time and cost in assembly process and to increase the quality of products, it is very important to choose an optimal assembly sequence. In this paper, we propose a methodology that generates an optimal assembly sequence by using the knowledge of experts. First, a product is divided into several sub-assemblies. Next, the disassembly sequences of sub-assembly are generated using disassembly rules and special information can be extracted through the disassembly process. By combining every assembly sequence of sub-assemblies, we can generate all the possible assembly sequences of a product. Finally, the expert system evaluates all the possible assembly sequences and finds an optimal assembly sequence. It can be achieved under consideration of the parameters such as assembly operation, tool change, safety of part. basepart location, setup change, distance, and orientation. The developed system is applied to UBR(Unit Bath Room) example.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.56
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• pp.9-18
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• 2000

This paper considers the problem of two-machine, M-parallel flow shop scheduling to minimize makespan, and proposes a series of heuristic algorithms and a branch and bound algorithm. Two processing times of each job at two machines on each line are identical on any line. Since each flow-shop line consists of two machines, Johnson's sequence is optimal for each flow-shop line. Heuristic algorithms are developed in this paper by combining a "list scheduling" method and a "local search with global evaluation" method. Numerical experiments show that the proposed heuristics can efficiently give optimal or near-optimal schedules with high accuracy. with high accuracy.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.12
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• pp.1156-1166
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• 2004

The shape of plate-fin type heat sink is numerically optimized to acquire the minimum pressure drop under the required temperature rise. In constrained nonlinear optimization problems of thermal/fluid systems, three fundamental difficulties such as high computational cost for function evaluations (i.e., pressure drop and thermal resistance), the absence of design sensitivity information, and the occurrence of numerical noise are commonly confronted. Thus, a sequential approximate optimization (SAO) algorithm has been introduced because it is very hard to obtain the optimal solutions of fluid/thermal systems by means of gradient-based optimization techniques. In this study, the progressive quadratic response surface method (PQRSM) based on the trust region algorithm, which is one of sequential approximate optimization algorithms, is used for optimization and the heat sink is optimized by combining it with the computational fluid dynamics (CFD).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.287-293
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• 2013

Cognitive radio has been recently proposed to dynamically access unused-spectrum. The CR users can share the same frequency band with the primary user without interference to each other. Usually each CR user needs to determine spectrum availability by itself depending only on its local observations. But uncertainty communication environment effects can be mitigated so that the detection probability is improved in a heavily shadowed environment. Soft detection is a primary user detection method of cooperative cognitive radio networks. In our research, we will improve system detection probability by using optimal cooperative node selection algorithm. New algorithm can find optimal number of cooperative sensing nodes for cooperative soft detection by using maximum ratio combining (MRC) method. Through analysis, proposed cooperative node selection algorithm can select optimal node for cooperative sensing according to the system requirement and improve the system detection probability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.4
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• pp.394-401
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• 2013

Recently there have been much interest in the performance improvement of maritime communication system. In the maritime communication system, the wireless channel is likely to be time-invariant and the retransmission scheme is not proper because it does not provide time diversity. For the improvement of reliability, we consider MIMO ARQ scheme using Alamouti-type signal which can provide space and time diversity. In this paper, we also propose the criterion of optimal retransmission order and provide its performance of error probability and packet throughput. The proposed MIMO ARQ scheme with optimal retransmission order has performance gain over random ordered MIMO ARQ and conventional Chase combining method. Therefore we expect that it can be adapted to the next generation maritime communication system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5631-5652
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• 2019

The existing defense strategy selection methods based on game theory basically select the optimal defense strategy in the form of mixed strategy. However, it is hard for network managers to understand and implement the defense strategy in this way. To address this problem, we constructed the incomplete information stochastic game model for the dynamic analysis to predict multi-stage attack-defense process by combining Bayesian game theory and the Markov decision-making method. In addition, the payoffs are quantified from the impact value of attack-defense actions. Based on previous statements, we designed an optimal defense strategy selection method. The optimal defense strategy is selected, which regards defense effectiveness as the criterion. The proposed method is feasibly verified via a representative experiment. Compared to the classical strategy selection methods based on the game theory, the proposed method can select the optimal strategy of the multi-stage attack-defense process in the form of pure strategy, which has been proved more operable than the compared ones.

• Journal of IKEEE
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• v.11 no.1 s.20
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• pp.1-14
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• 2007

In this paper, a design of an integral augmented nonlinear optimal variable structure system(INOVSS) is presented for the prescribed output control of uncertain MIMO systems under persistent disturbances. This algorithm basically concerns removing the problems of the reaching phase and combining with the nonlinear optimal control theory. By means of an integral nonlinear sliding surface, the reaching phase is completely removed. The ideal sliding dynamics of the integral nonlinear sliding surface is obtained in the form of the nonlinear state equation and is designed by using the nonlinear optimal control theory, which means the design of the integral nonlinear sliding surface and equivalent control input. The homogeneous $2{\upsilon}(\kappa)$ form is defined in order to easily select the $2{\upsilon}$ or even $(\kappa)-form$ higher order nonlinear terms in the suggested sliding surface. The corresponding nonlinear control input is designed in order to generate the sliding mode on the predetermined transformed new surface by means of diagonalization method. As a result, the whole sliding output from a given initial state to origin is completely guaranteed against persistent disturbances. The prediction/predetermination of output is enable. Moreover, the better performance by the nonlinear sliding surface than that of the linear sliding surface can be obtained. Through an illustrative example, the usefulness of the algorithm is shown.

• Journal of Korean Institute of Industrial Engineers
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• v.35 no.1
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• pp.1-14
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• 2009

The efficiency of transportation requests fulfillment can be increased through extending the problem of vehicle routing and scheduling by the possibility of subcontracting a part of the requests to external carriers. This problem extension transforms the usual vehicle routing and scheduling problems to the more general integrated operational transportation problems. In this contribution, we analyze the motivation, the chances, the realization, and the challenges of the integrated operational planning and report on experiments for extending the plain Vehicle Routing Problem to a corresponding problem combining vehicle routing and request forwarding by means of different sub-contraction types. The extended problem is formalized as a mixed integer linear programming model and solved by a commercial mathematical programming solver. The computational results show tremendous costs savings even for small problem instances by allowing subcontracting. Additionally, the performed experiments for the operational transportation planning are used for an analysis of the decision on the optimal fleet size for own vehicles and regularly hired vehicles.