• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.4
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• pp.53-62
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• 2009

Lee[15] examined quantity discount contracts between a manufacturer and a retailer in a stochastic, two-period inventory model where quantity discounts are provided based on the previous order size. During the two periods, the retailer faces stochastic (truncated Poisson distributed) demands and he/she places orders to meet the demands. The manufacturer provides for the retailer a price discount for the second period order if its quantity exceeds the first period order quantity. In this paper we extend the above two-period model to a k-period one (where k < 2) and propose a stochastic nonlinear mixed binary integer program for it. In order to make the program tractable, the nonlinear term involving the sum of truncated Poisson cumulative probability function values over a certain range of demand is approximated by an i-interval piecewise linear function. With the value of i selected and fixed, the piecewise linear function is determined using an evolutionary algorithm where its fitness to the original nonlinear term is maximized. The resulting piecewise linear mixed binary integer program is then transformed to a mixed binary integer linear program. With the k-period model developed, we suggest a solution procedure of receding horizon control style to solve n-period (n < k) order decision problems. We implement Lee's two-period model and the proposed k-period model for the use in receding horizon control style to solve n-period order decision problems, and compare between the two models in terms of the pattern of order quantities and the total profits. Our computational study shows that the proposed model is superior to the two-period model with respect to the total profits, and that order quantities from the proposed model have higher fluctuations over periods.

• IE interfaces
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• v.21 no.3
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• pp.343-353
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• 2008

Vendor-managed inventory(VMI) is a supply chain strategy to improve the inventory turnover and customer service in supply chain management. Unfortunately, many VMI programs fail because they simply transfer the transactional aspects of placing replenishment orders from customer to vendor. In fact, such VMI programs often degrade supply chain performance because vendors lack capability to plan the VMI operations effectively in an integrated way under the dynamic, complex, and stochastic VMI supply chain environment. This paper presents a decision support system, termed DSSV, for VMI in the retail supply chain. DSSV supports the market forecasting, vendor's production planning, retailer's inventory replenishment planning, vehicle routing, determination of the system operating parameter values, retailer's purchase price decision, and what-if analysis. The potential benefits of DSSV include the provision of guidance, solution, and simulation environment for enterprises to reduce risks for their VMI supply chain operations.

• Journal of The Korean Astronomical Society
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• v.34 no.4
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• pp.231-235
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• 2001

Cosmic-ray acceleration, although physically important in many astrophysical contexts, is difficult to incorporate into numerical models,. because it involves microphysics that is generally far from thermodynamic equilibrium, and also because the length and time scales for that physics typically range over many orders of magnitude, reflecting the huge range of particle rigidities that must be represented. The most common accelerator models are stochastic in nature and involve nonequilibrium plasma properties that are also often poorly understood. Still, nature clearly finds a way to produce simple, robust and almost scale-free energy distributions for the cosmic-rays. Their importance has inspired a number of approaches to examining the production and transport of cosmic-ray particles in numerical simulations. I offer here a brief comparison of some of the methods that have been introduced.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.10a
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• pp.65-65
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• 1998

Manufacturing systems like a motor production process are analyzed using simulations than numerical analyses and/or heuristic methods due to their stochastic properties. The SME(small and medium enterprise) producing automotive motors that develop CIM systems to improve production performance is focused as an application site. We analyze and understand the system exactly using layout based simulation, and then we will suggest the initial feashible production-plan dependent on the layout to overcome weak-points of the current system(i.e., high WIPs, bottle-neck processes, due-date delays and etc.). And, solutions are suggested to increase performances of SMEs producing automotive motors in this paper. The simulation model built in this study is moedlled and analyzed with fully object-oriented methodology using SiMPLE++TM according to properties of production processes of the automotive motor. And, we will introduce ways to verify the model with developed templates for reusability when new needs will be occurred such as designing a new ship, extension or rearrangement of the system, change of production-plans, receiving urgent orders, and so on.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1319-1325
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• 2010

Quantile regression provides a more complete statistical analysis of the stochastic relationships among random variables. Sometimes quantile functions estimated at different orders can cross each other. We propose a new non-crossing quantile regression method applying support vector median regression to restricted regression quantile, restricted support vector quantile regression. The proposed method provides a satisfying solution to estimating non-crossing quantile functions when multiple quantiles for high dimensional data are needed. We also present the model selection method that employs cross validation techniques for choosing the parameters which aect the performance of the proposed method. One real example and a simulated example are provided to show the usefulness of the proposed method.

• Industrial Engineering and Management Systems
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• v.12 no.2
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• pp.85-94
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• 2013

This research investigates the supply chain contract between a distributor and a supplier in which the selling period is relatively short in comparison with long production lead time. At the first stage, supplier who is a Stackelberg leader offers the distributor a contract with a set of parameters, and subjected to those parameters, the distributor places the number of initial orders as well as options. In order to purchase the option, the distributor pays non-linear option premium price with respect to the number of purchased options. At the second stage, based on realized demand, the distributor has the right to exercise option as either put or call which is limited up to the number of purchased options. The wholesale price contract is used as a benchmarking contract. This research has confirmed that the supply chain contract with a non-linear option premium price can help to coordinate the supply chain.

• Journal of Korea Port Economic Association
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• v.31 no.3
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• pp.1-14
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• 2015

This study utilizes Monte Carlo simulation to forecast the time charter rate of vessels, the three-month Libor interest rate, and the ship demolition price, to mitigate future uncertainties involving these factors. The simulation was performed 10,000 times to obtain an exact result. For the empirical analysis - based on considerations in ordering ships in 2010-a comparison between the Monte Carlo simulation-based stochastic discounted cash flow (DCF) method and traditional DCF methods was made. The analysis revealed that the net present value obtained through Monte Carlo simulation was lower than that obtained via regular DCF methods, alerting the owners to risks and preventing them from placing injudicious orders for ships. This research has implications in reducing the uncertainties that future shipping markets face, through the use of a stochastic DCF approach with relevant variables and probability methods.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.243-250
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• 1999

We consider $Y=X{\lambda}Z,\;{\lambda}>0$, where X and Z are independent random variables, and Y is the length biased distribution or the equilibrium distribution of X. The purpose of this paper is to consider the distribution of X or Y when the distribution of Z is given and the distribution of Z when the distribution of X or Y is given, In particular, we obtain that the necessary and sufficient conditions for X to be $X^{2}({\upsilon})\;is\;Z{\sim}X^{2}(2)\;and\;for\;Z\;to\;be\;X^{2}(1)\;is\;X{\sim}IG({\mu},\;{\mu}^{2}/{\lambda})$, where $IG({\mu},\;{\mu}^{2}/{\lambda})$ is two-parameter inverse Gaussian distribution. Also we show that X is smaller than Y in the reverse Laplace transform ratio order if and only if $X_{e}$ is smaller than $Y_{e}$ in the Laplace transform ratio order. Finally, we can get the results that if X is smaller than Y in the Laplace transform ratio order, then $Y_{L}$ is smaller than $X_{L}$ in the Laplace transform order, and that if X is smaller than Y in the reverse Laplace transform ratio order, then $_{\mu}X_{L}$ is smaller than $_{\nu}Y_{L}$ in the Laplace transform order.

• Structural Engineering and Mechanics
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• v.84 no.3
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• pp.323-335
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• 2022

The main idea of the framework is to seamlessly combine a reasonably accurate and fast surrogate model with the importance sampling strategy. Developing a surrogate model for predicting structures' dynamic responses is challenging because it involves high-dimensional inputs and outputs. For this purpose, a novel surrogate model based on cutting-edge deep learning architectures specialized for capturing temporal relationships within time-series data, namely Long-Short term memory layer and Transformer layer, is designed. After being properly trained, the surrogate model could be utilized in place of the finite element method to evaluate structures' responses without requiring any specialized software. On the other hand, the importance sampling is adopted to reduce the number of calculations required when computing the failure probability by drawing more relevant samples near critical areas. Thanks to the portability of the trained surrogate model, one can integrate the latter with the Importance sampling in a straightforward fashion, forming an efficient framework called TTIS, which represents double advantages: less number of calculations is needed, and the computational time of each calculation is significantly reduced. The proposed approach's applicability and efficiency are demonstrated through three examples with increasing complexity, involving a 1D beam, a 2D frame, and a 3D building structure. The results show that compared to the conventional Monte Carlo simulation, the proposed method can provide highly similar reliability results with a reduction of up to four orders of magnitudes in time complexity.