• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.77-85
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• 2013

In the location science, environmental effect becomes a new main consideration for site selection. For the unwanted facility location selection, decision makers should consider the cost of resolving the environmental conflict. We introduced the negative influence cost for the facility which was inversely proportional to distance between the facility and residents. An unwanted facility location problem was suggested to minimize the sum of the negative influence cost and the transportation cost. The objective cost function was analyzed as nonlinear type and was neither convex nor concave. Three GRASP (Greedy Randomized adaptive Search Procedure) methods as like Random_GRASP, Epsilon_GRASP and GRID_GRASP were developed to solve the unwanted facility location problem. The Newton's method for nonlinear optimization problem was used for local search in GRASP. Experimental results showed that quality of solution of the GRID_GRASP was better than those of Random_GRASP and Epsilon_GRASP. The calculation time of Random_GRASP and Epsilon_GRASP were faster than that of Grid_GRASP.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.213-219
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• 2005

In order to move NPC's to target location at game maps, various algorithm including A* has been used. The most frequently used algorithm among them is A* with fast finding speed. But A* has the following problems. The first problem is that at randomly changing map, it is necessary to calculate all things again whenever there are any changes. And when calculation is wrong, it is not possible to search for target. The second problem is that it is difficult to move avoiding dangerous locations damaging NPC such as an obstruction. Although it is possible to avoid moving to locations with high weight by giving weight to dangerous factors. it is difficult to control in case NPC moves nearby dangerous factors. In order to solve such problems, in this thesis, the researcher applied Dynamic Programming to path-finding algorithm. As the result of its application, the researcher could confirm that the programming was suitable for changes at the map with random change and NPC's avoided the factors being dangerous to them far away. In addition. when compared to A*, there were good results.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.3
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• pp.23-36
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• 2016

We consider a model that minimizes the total cost incurred by assigning available weapons to existing targets in order to reduce enemy threats, which is called the weapon target assignment problem (WTAP). This study addresses the stochastic versions of WTAP, in which data, such as the probability of destroying a target, are given randomly (i.e., data are identified with certain probability distributions). For each type of random data or parameter, we provide a stochastic optimization model on the basis of the expected value or scenario enumeration. In particular, when the probabilities of destroying targets depending on weapons are stochastic, we present a stochastic programming formulation with a simple recourse. We show that the stochastic model can be transformed into a deterministic equivalent mixed integer programming model under a certain discrete probability distribution of randomness. We solve the stochastic model to obtain an optimal solution via the mixed integer programming model and compare this solution with that of the deterministic model.

• Journal of the Korea Society for Simulation
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• v.5 no.2
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• pp.85-93
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• 1996

Simulation is commonly used to find the best values of decision variables for problems which defy analytical solutions. This objective is similar to that of optimization problems and thus, mathematical programming techniques may be applied to simulation. However, the application of mathematical programming techniques, e.g., the gradient methods, to simulation is compounded by the random nature of simulation responses and by the complexity of the statistical issues involved. In this paper, therefore, we explain the Reverse-Simulation method to optimize a simulation model in a single simulation run. First, we point the problem of the previous Reverse-Simulation method. Secondly, we propose the new algorithm to solve the previous method and show the efficiency of the proposed algorithm.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.19 no.4
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• pp.417-428
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• 2015

Although, in general, the random fluctuation of interest rates gives a limited impact on portfolio optimization, their stochastic nature may exert a significant influence on the process of selecting the proportions of various assets to be held in a given portfolio when the stochastic volatility of risky assets is considered. The stochastic volatility covers a variety of known models to fit in with diverse economic environments. In this paper, an optimal strategy for portfolio selection as well as the smoothness properties of the relevant value function are studied with the dynamic programming method under a market model of both stochastic volatility and stochastic interest rates.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.106-108
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• 1987

Until now, inflow has been handled an independent log-normal random variable in the problem of planning the long-term operation of a multi-reservoir hydrothermal electric power generation system. This paper introduces the detail study for making rule curve by applying weekly time interval for handling inflows. The hydro system model consists of a set of reservoirs and ponds. Thermal units are modeld by one equivalent thermal unit. Objective is minimizing the total cost that the summation of the fuel cost of equivalent thermal unit at each time interval. For optimization, stochastic dynamic programming(SDP) algorithm using successive approximations is used.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.37
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• pp.201-210
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• 1996

Although FMS implementation in Korea is not yet mature, the worldwide empirical data shows the diffusion of FMS is inevitable in near future. As the reletionships between the high capital cost and the relative benefits and advantages are complex to analyse, it is rather beneficial to prepare the effective operation strategies which exploit the FMS flexibility, such as machine loading with alternative routing and dispatching rules. This paper shows the formulation applying a goal programming model for the loading problem with objectives of minimizing the production cost and maximizing the machine utilization, including constraints such as machine tool capacity and demands, etc. A realistic random FMS model is developed for illustration. Since loading and dispatching are a composite of two interdependent tasks, simulation is made to investigate the interactions between the two.

• Wind and Structures
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• v.11 no.3
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• pp.241-256
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• 2008

This paper adopts autoregressive (AR) model to simulate the wind velocity of spatial three-dimensional fields in accordance with the time and space dependent characteristics of the 3-D fields. Based on the built MATLAB programming, this paper discusses in detail the issues of the AR model deduced by matrix form in the simulation and proposes the corresponding solving methods: the over-relaxation iteration to solve the large sparse matrix equations produced by large number of degrees of freedom of structures; the improved Gauss formula to calculate the numerical integral equations which integral functions contain oscillating functions; the mixed congruence and central limit theorem of Lindberg-Levy to generate random numbers. This paper also develops a method of ascertaining the rank of the AR model. The numerical examples show that all those methods are stable and reliable, which can be used to simulate the wind velocity of all large span structures in civil engineering.

• Steel and Composite Structures
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• v.50 no.4
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• pp.443-458
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• 2024

The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce CO₂ emissions in the building industry. In the present work, the compressive strength (f_c) of GPC is calculated using random forests regression (RFR) methodology where natural zeolite (NZ) and silica fume (SF) replace ground granulated blast-furnace slag (GGBFS). From the literature, a thorough set of experimental experiments on GPC samples were compiled, totaling 254 data rows. The considered RFR integrated with artificial hummingbird optimization (AHA), black widow optimization algorithm (BWOA), and chimp optimization algorithm (ChOA), abbreviated as ARFR, BRFR, and CRFR. The outcomes obtained for RFR models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For R² metric, the CRFR model gained 0.9988 and 0.9981 in the train and test data set higher than those for BRFR (0.9982 and 0.9969), followed by ARFR (0.9971 and 0.9956). Some other error and distribution metrics depicted a roughly 50% improvement for CRFR respect to ARFR.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.4
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• pp.137-146
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• 2016

In recent years, business environment is faced with multi uncertainty that have not been suffered in the past. As supply chain is getting expanded and longer, the flow of information, material and production is also being complicated. It is well known that development service industry using application software has various uncertainty in random events such as supply and demand fluctuation of developer's capcity, project effective date after winning a contract, manpower cost (or revenue), subcontract cost (or purchase), and overrun due to developer's skill-level. This study intends to social contribution through attempts to optimize enterprise's goal by supply chain management platform to balance demand and supply and stochastic programming which is basically applied in order to solve uncertainty considering economical and operational risk at solution supplier. In Particular, this study emphasizes to determine allocation of internal and external manpower of developers using S&OP (Sales & Operations Planning) as monthly resource input has constraint on resource's capability that shared in industry or task. This study is to verify how Stochastic Programming such as Markowitz's MV (Mean Variance) model or 2-Stage Recourse Model is flexible and efficient than Deterministic Programming in software enterprise field by experiment with process and data from service industry which is manufacturing software and performing projects. In addition, this study is also to analysis how profit and labor input plan according to scope of uncertainty is changed based on Pareto Optimal, then lastly it is to enumerate limitation of the study extracted drawback which can be happened in real business environment and to contribute direction in future research considering another applicable methodology.