• Korean Management Science Review
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• v.9 no.1
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• pp.87-92
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• 1992

The purpose of this paper is to study the sensitivity analysis in the matrix game. The third type sensitivity analysis is defined as finding the characteristic region of an element of the payoff matrix in which the set of current active strategies is preserved. First by using the relationship between matrix game and linear programming, we induce the conditions which must be satisfied for preserving the set of current active strategies. Second we show the characteristic regions of active and inactive strategy. It is found that the characteristic regions we suggests in this paper are same with that of the type one sensitivity analysis suggested by Sung[3] except only one case.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.11
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• pp.668-674
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• 2003

The electric marketplace is in the midst of major changes designed to promote competition. No longer vertically integrated with guaranteed customers and suppliers, electric generators and distributors will have to compete to sell and buy electricity. Unit commitment (UC) in such a competitive environment is not the same as the traditional one anymore. The objective of UC is not to minimize production cost as before but to find the solution that produces a maximum profit for a generation firm. This paper presents a hi-level formulation that decomposes the UC game into a generation-decision game (first level game) and a state(on/off)-decision game (second level game). Derivation that the first-level game has a pure Cournot Nash equilibrium(NE) helps to solve the second-level game. In case of having a mixed NE in the second-level game, this paper chooses a pure strategy having maximum probability in the mixed strategy in order to obviate the probabilistic on/off state which may be infeasible. Simulation results shows that proposed method gives the adequate UC solutions corresponding to a NE.

• Journal of the Korea Society of Computer and Information
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• v.21 no.6
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• pp.107-115
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• 2016

In this paper, we propose a Bicycle sharing has grown popular around the cities in the world due to its convenience. However, the bicycle sharing system is not problem-free, and there remains many managerial problems to be solved. In this study, we analyzed pricing strategy of a bicycle sharing system by minimizing the number of bicycles relocated by trucks, the act of which incurs penalty. The objective function is constructed by applying mixed integer programming and is presented as a stochastic model by using Markov chain so that arrival and departure rates of bicycle stations can be utilized in the analysis. The efficiency of the presented model is verified upon the analysis of bicycle sharing data gathered in Daejeon in 2014.

• Journal of Korean Port Research
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• v.12 no.2
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• pp.217-224
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• 1998

During the operation of crane system in container yard, the objective is to transport the load to a goal position as quick as possible without rope oscillation. The container crane is generally operated by an expert operator, but recently an automatic control system with high speed and rapid transportation is required. Therefore, we developed an optimal controller which has to control the crane system with disturbances. In this paper, we present a design of optima 2-DOF PID controller for the control of gantry crane which has to control swing motion and trolley position. We used evolution strategy(ES) to tune the parameters of 2-DOF PID controller. It was compared with general PID controller. The computer simulations show that the proposed method has better performances than the other method.

• Bulletin of the Korean Mathematical Society
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• v.56 no.6
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• pp.1467-1483
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• 2019

This paper analyzes a robust optimal reinsurance and investment strategy for an Ambiguity-Averse Insurer (AAI), who worries about model misspecification and insists on seeking robust optimal strategies. The AAI's surplus process is assumed to follow a jump-diffusion model, and he is allowed to purchase proportional reinsurance or acquire new business, meanwhile invest his surplus in a risk-free asset and a risky-asset, whose price is described by an Ornstein-Uhlenbeck process. Under the criterion for maximizing the expected exponential utility of terminal wealth, robust optimal strategy and value function are derived by applying the stochastic dynamic programming approach. Serval numerical examples are given to illustrate the impact of model parameters on the robust optimal strategies and the loss utility function from ignoring the model uncertainty.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.904-913
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• 2005

This paper deals with an integrated problem of inventory control and dynamic pricing strategies for a commodity with price and sales-period dependent demand pattern, where a seller and customers have complete information of each other. The problem consists of two parts; one is each buyer's benefit problem which makes the best decision on price and time for buyer to purchase items, and the other one is a seller's profit problem which decides an optimal sales strategy concerned with inventory control and discount schedule. The seller's profit function consists of sales revenue and inventory holding cost functions. The two parts are closely related into each other with some related variables, so that any existing general solution methods can not be applied. Therefore, a simplified model with single seller and two customers in considered first, where demand for multiple units is allowed to each customer within a time limit. Therewith, the model is generalized for a n-customer-classes problem. To solve the proposed n-customer-set problem, a dynamic programming algorithm is derived. In the proposed dynamic programming algorithm, an intermediate profit function is used, which is computed in case of a fixed initial inventory level and then adjusted in searching for an optimal inventory level. This leads to an optimal sales strategy for a seller, which can derive an optimal decision on both an initial inventory level and a discount schedule, in $O(n^2)$ time. This result can be used for some extended problems with a small customer set and a short selling period, including sales strategy for department stores, Dutch auction for items with heavy holding cost, open tender of materials, quantity-limited sales, and cooperative buying in the on/off markets.

• Nuclear Engineering and Technology
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• v.27 no.5
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• pp.721-729
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• 1995

The Korean optimal nuclear fuel cycle strategy from the year 2000 to 2030 is derived using linear programming. The fuel cycle cost, the cost uncertainty, and the natural uranium consumption are used as the criteria for the optimization. These objectives are compromised by fuzzy decision-making technique which maximizes the minimum degree of satisfaction among the three objectives. The options for the back-end fuel cycle are direct disposal, reprocessing, and DUPIC. The optimal fuel cycle strategy of Korea is to start reprocessing in around 2010 and increase its capacity with the maximum of 800 tHM in around 2025, and to star DUPIC processing in 2025. The cot uncertainty and the natural uranium consumption of the optimal fuel cycle strategy are reduced by 7.1％ and 6.1％, respectively, at the cost penalty of 5.4％ compared with the cost-only optimal solution.

• Korean Management Science Review
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• v.21 no.3
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• pp.101-113
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• 2004

In the rapidly propagating Internet based electronic transaction environment. the importance of real time order confirmation has been more emphasized, In this paper, using data mining techniques, we develop intelligent operations decision model to allow real time order confirmation at the time the customer places an order with required delivery terms. Among various operation plannings used for order fulfillment. mill routing is the first interface decision point to link the order receiving at the marketing with the production planning for order fulfillment. Though linear programming based mathematical optimization techniques are mostly used for mill routing problems, some early orders should wait until sufficient orders are gathered for optimization. And that could effect longer order fulfillment lead-time, and prevent instant order confirmation of delivery terms. To cope with this problem, we provide the intelligent decision model to allow instant order based mill routing decisions. Data mining techniques of decision trees and neural networks. which are more popular in marketing and financial applications, are used to develop the model. Through diverse computational trials with the industrial data from the steel company. we have reported that the performance of the proposed approach is effective compared to the present heuristic only mill routing results. Various issues of data mining techniques application to the mill routing problems having linear programming characteristics are also discussed.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.392-392
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• 2000

This study performs optimization of paper mill scheduling using MINLP(Mixed-Integer Non-Linear Programming) method and 2-step decomposing strategy. Paper mill process is normally composed of five units: paper machine, coater, rewinder, sheet cutter and roll wrapper/ream wrapper. Various kinds of papers are produced through these units. The bottleneck of this process is how to cut product papers efficiently from raw paper reel and this is called trim loss problem or cutting stock problem. As the trim must be burned or recycled through energy consumption, minimizing quantity of the trim is important. To minimize it, the trim loss problem is mathematically formulated in MINLP form of minimizing cutting patterns and trim as well as satisfying customer's elder. The MINLP form of the problem includes bilinearity causing non-linearity and non-convexity. Bilinearity is eliminated by parameterization of one variable and the MINLP form is decomposed to MILP(Mixed-Integer Linear programming) form. And the MILP problem is optimized by means of the optimization package. Thus trim loss problem is efficiently minimized by this 2-step optimization method.

• Computational Structural Engineering
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• v.3 no.1
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• pp.83-95
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• 1990

Many structural optimization problems are solved by numerical algorithms since these are complicated and nonlinear. To provide a wider base and popular it to structual design optimization, reliable, accurate and superlinearly convergent nonlinear programming algorithm with active-set strategy have been developed. One of these is RQP(recursive quadratic programming method). This algorithm has several parameters and its performance is influenced by variations of these key parameters. Therefore, an RQP algorithm is selected to enhance its numerical performances by choosing proper parameters. The paper persents these influences on its numerical performance. For comparison of performances, a structural design software for minimum weight of truss subjected to displacement, stress, and lower and upper bounds on design variables is also implemented.